{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization which was recently proposed by [3].\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [3] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [3] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[3] Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "from time import clock\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: Forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ -2.3814598  -13.18038246   1.91780462]\n",
      "  stds:  [ 27.18502186  34.21455511  37.68611762]\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  mean:  [  2.22044605e-17   8.16013923e-17   4.57966998e-17]\n",
      "  std:  [ 0.99999999  1.          1.        ]\n",
      "After batch normalization (nontrivial gamma, beta)\n",
      "  means:  [ 11.  12.  13.]\n",
      "  stds:  [ 0.99999999  1.99999999  2.99999999]\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization\n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print('  means: ', a.mean(axis=0))\n",
    "print('  stds: ', a.std(axis=0))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, np.ones(D3), np.zeros(D3), {'mode': 'train'})\n",
    "print('  mean: ', a_norm.mean(axis=0))\n",
    "print('  std: ', a_norm.std(axis=0))\n",
    "\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print('After batch normalization (nontrivial gamma, beta)')\n",
    "print('  means: ', a_norm.mean(axis=0))\n",
    "print('  stds: ', a_norm.std(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.03927354 -0.04349152 -0.10452688]\n",
      "  stds:  [ 1.01531428  1.01238373  0.97819988]\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "for t in range(50):\n",
    "  X = np.random.randn(N, D1)\n",
    "  a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "  batchnorm_forward(a, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print('  means: ', a_norm.mean(axis=0))\n",
    "print('  stds: ', a_norm.std(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.70292412915e-09\n",
      "dgamma error:  7.42041421625e-13\n",
      "dbeta error:  2.87950576558e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Normalization: alternative backward (OPTIONAL, +3 points extra credit)\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For the sigmoid function, it turns out that you can derive a very simple formula for the backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can also derive a simple expression for the batch normalization backward pass if you work out derivatives on paper and simplify. After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster.\n",
    "\n",
    "NOTE: This part of the assignment is entirely optional, but we will reward 3 points of extra credit if you can complete it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx difference:  1.41063543819e-12\n",
      "dgamma difference:  0.0\n",
      "dbeta difference:  0.0\n",
      "speedup: 2.30x\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = clock()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = clock()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = clock()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs2312n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the flag `use_batchnorm` is `True` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.26119551013\n",
      "W1 relative error: 1.10e-04\n",
      "W2 relative error: 2.85e-06\n",
      "W3 relative error: 3.92e-10\n",
      "b1 relative error: 4.44e-08\n",
      "b2 relative error: 2.22e-08\n",
      "b3 relative error: 4.78e-11\n",
      "beta1 relative error: 7.33e-09\n",
      "beta2 relative error: 1.89e-09\n",
      "gamma1 relative error: 7.57e-09\n",
      "gamma2 relative error: 1.96e-09\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.99653322011\n",
      "W1 relative error: 1.98e-06\n",
      "W2 relative error: 2.28e-06\n",
      "W3 relative error: 1.11e-08\n",
      "b1 relative error: 5.55e-09\n",
      "b2 relative error: 5.55e-09\n",
      "b3 relative error: 2.23e-10\n",
      "beta1 relative error: 6.65e-09\n",
      "beta2 relative error: 3.48e-09\n",
      "gamma1 relative error: 5.94e-09\n",
      "gamma2 relative error: 4.14e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            use_batchnorm=True)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  if reg == 0: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 200) loss: 2.340974\n",
      "(Epoch 0 / 10) train acc: 0.105000; val_acc: 0.111000\n",
      "(Epoch 1 / 10) train acc: 0.300000; val_acc: 0.244000\n",
      "(Epoch 2 / 10) train acc: 0.431000; val_acc: 0.315000\n",
      "(Epoch 3 / 10) train acc: 0.464000; val_acc: 0.293000\n",
      "(Epoch 4 / 10) train acc: 0.565000; val_acc: 0.331000\n",
      "(Epoch 5 / 10) train acc: 0.568000; val_acc: 0.313000\n",
      "(Epoch 6 / 10) train acc: 0.654000; val_acc: 0.331000\n",
      "(Epoch 7 / 10) train acc: 0.693000; val_acc: 0.328000\n",
      "(Epoch 8 / 10) train acc: 0.690000; val_acc: 0.323000\n",
      "(Epoch 9 / 10) train acc: 0.775000; val_acc: 0.322000\n",
      "(Epoch 10 / 10) train acc: 0.779000; val_acc: 0.320000\n",
      "(Iteration 1 / 200) loss: 2.302332\n",
      "(Epoch 0 / 10) train acc: 0.123000; val_acc: 0.130000\n",
      "(Epoch 1 / 10) train acc: 0.264000; val_acc: 0.212000\n",
      "(Epoch 2 / 10) train acc: 0.320000; val_acc: 0.298000\n",
      "(Epoch 3 / 10) train acc: 0.343000; val_acc: 0.275000\n",
      "(Epoch 4 / 10) train acc: 0.397000; val_acc: 0.318000\n",
      "(Epoch 5 / 10) train acc: 0.445000; val_acc: 0.314000\n",
      "(Epoch 6 / 10) train acc: 0.486000; val_acc: 0.337000\n",
      "(Epoch 7 / 10) train acc: 0.558000; val_acc: 0.310000\n",
      "(Epoch 8 / 10) train acc: 0.605000; val_acc: 0.321000\n",
      "(Epoch 9 / 10) train acc: 0.620000; val_acc: 0.345000\n",
      "(Epoch 10 / 10) train acc: 0.669000; val_acc: 0.310000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "bn_solver.train()\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i9O/leMEsMRERecEMHZEJu0yZWQDhNgPkRpg3c0m9UXQ6bv3jbv+LZpb1nNa+\nxfL5AiTqmlEyMSNKREQFbjN0DOgoWfZsHFnnZrqezOZxL8GM3U3Vmif3Opb4xfEGLKk3il6P200J\nJqAFaPs6F3t+bhKuGSXXeCq7JiIiexwsTpVnz0bgiRvzc++U9ucTN2rbHR732treroOmm+5+Xrss\n+hnc7Pa5Se0G6fW4zcZZmDFr6uHmuUm4ZpRc7CpKRERecQ0dJcfPVwI5w01NLqttb26zfXzNn+/0\nvDbKao2XVdc/I7c3YH4GN3t5blJvFL0et3HNV10mjWMDg8gNjVYjWK1LND7Xqn4h7teMkotdRYmI\nyCtm6Cg5jnTbb7d5PMhgxk8GyIyfzJmX5wY5+62cSjlu/ezD3bedjzWXnOl6XqH+uY0+r5mfzCuN\nT2b/fanUxkhERBQMBnSUHHVN9tttHg8ymGmd3Yj75/4ev57wZbx2whX41YQb8enqbUX7eLkB8xNs\nenluUm8Ugzhut8Ptg3xvr2W+RIB9uTcREZEZllxSvOmbnGROBapqgKGB0cfTGa3xCaD9+cSNxWWX\n+ceXDfkfB1BoqjLnTz9DZ833kcGfAQGm4BDWnPB91E1I49+PznPVCVHfoCUlgiGT5kRugk0v5VlB\nt58vV8fMKNvm+3lvjkCgUgU10oOIiMYHdrmk+Co0OdEHaKk0cMLJQPbtULtcGunXqnXV3Iim1KGx\nO9VNBW5+0dVrdT12N27CQ5gih9CrGrB6sA2bh1tG9nHbSTGqzpVJ7ZgZNLvvlNUIBLPumkRERERG\nHFtAybd2Zr5jpYHLwClI+lbir51wBVJitaeYB5o6Hd+4Dctzd6NWRjON/aoGtw7+HR4fml9SsKkP\nKs59/yT84pWDoWazrFqrV4lgWKlxMa/NKahl+3kiIiLyw21Ax5JLii+nJiheGbN3088HXn3Keqad\njn5NWq9qQJOYZOgAFI1LAExf75qBH6A2NVC0rVYG8P9WbcC3v3m759PSl2d57ZhZatbSau1eoXTU\nS6fOqJV6DZxKKs0G0Ae5ZtFvxjmJA+aJiIhoLDZFofhyaoLihdmMuh3ft55pV3jO2plARz2enfBl\nLEl1AQBWD7ahX9XYv19hnIKJKanDnrZ7YRVkfGXj82M6Lfpp2uFmfV85Z/GVys81cGpIE2ZzCz/H\nzWYtRERElYUBHcXXwhVaUxM9fRMUL8xm1BnpgzBDADgZB3FHej2WpLqwebgF7blr0KMaoGBZe2mZ\nSTyemWxQBNdVAAAgAElEQVS/XRdIYu3M4iDTgV3mzHjz7mdcgtvRDV5n8ZU7yPBzDdx0Ti21u6aT\nco26ICIiovhjQEfx1dwGXHSntmYOov150Z2WZZG23JZpFvYzCQAzMoBba34EAbDzlPOwvfUZSEdf\n/vhMSMo0KKu9cCUGqyYU7TpYNQG1F640zyQaM4c2vGTO/IxLMGafqsQ8sA1rFl9Q2Tw/1yDKMRDl\nGnVBRERE8cc1dBRvzW2lBXBGdU3mDVbM9gMsA8DJODS2Q6HZuAQAUPkAxbimrrlN+xdPt56vurB+\nb+3Msa9TyBy6uA5m67bMFNZOuR15YMZu7R4Q3iw+r+sE7fi5BlGOU7A67pQIprVvsT0Wv587ERER\nxQszdDQ+mJVvGunLOb2s3zNmEsWkFNG4pq65TevU2dGn/VkI1nw2gvGSOQsyw+R3vZiXwe9Blgz6\nvQZhlVQ6sSp5NSutdfPcJAyYJyIiInPM0NH4UAiY3Ha5NMu6pdLAwDGtjNK4vz6T2FFvfgxugjKr\nTKKHRjBuM2dBZ5j8DEP20hEyyJLBKLNsfhiP22w4vdUQ86SeMxEREZlznEMnIvcC+CSAN5VSM00e\nXwbgyvyP1QA+AGCSUuotEXkdwDsAhgAMupmjAHAOHcWEfsxB5lRg4CgwpBs3kM6Yr+nzMz/PbJi6\n1fu4FFWLeq/v63Z/s/luS1JduLXmR5iMQ44jKCoRh5gTERFVnsAGi4vIRwEcBXC/WUBn2PciADcr\npRbkf34dwByllNXQLlMM6Ch2vARpfoMy47y8MgYnQQV/TkO3/R6j/rWXpLpwR3o9MuIi2K5QHGJO\nRERUeQIbLK6UekZETnf5vp8B8EOX+xIlh5e1bWblnV6CsqAawXgUZLMRp6HbfhhLBm+t+REyKB7U\n7qWRTCUIe4i5HxxiTkREFK7A1tCJSC2ACwBcr9usADwtIkMAvqeUWmfz/GsBXAsAp512WlCHRRQM\nr2vbIgrK/AgyCAu7NX7Rer2OK813cjuqogLEdV1ckL8kICIiInNBNkW5CMA2pdRbum0tSqkeEXk3\ngJ+JyCtKqWfMnpwP9tYBWsllgMdF5J9Zk5RSh5zHVJBBWFlb4wfQSKYS+GlK45XbrFuYmVoiIiLS\nBDm24HIYyi2VUj35P98E8BiAeQG+H1H5BDnkvNz2bNTWAJoMOdfzMjrAiavW+C6Py5HZSIoKC7bj\npJB16+nLOo5I4BBzIiKi8AWSoROROgAfA3CVbtuJAFJKqXfyfz8fwEqLlyCKvwSWUY5p0GIccq5j\ntg7rkppfYaU8AnQc8LQW0LEE0MNxOfK7ZpE88ZJ1CzpTy/V4REREYzkGdCLyQwAfB9AgIt0AbgOQ\nBgCl1D353T4N4Cml1DHdU98D4DHRBhtXA3hQKfUfwR06ETn6+criMlHAsmGIMQi7+qTn8DW1HtXZ\n49oOHoMuYwngpl09mN+5Fb19WTw74VZMhrvjMmXWCdRpJEQZeA44DOex/X034KaXpsc6YPGSdQuy\nWQvX4xEREZlz0+XyMy72uQ/AfYZtrwE4s9QDI6IAeOnOCUMQtvYW4Mjx4h1K7B5pvBl/tzqoDUlz\ne7x6QWb3AuQ54DA5j5k7v4azctegBy1jnl/W7JTN6AwvWbcgm7WUsh6PGT0iIhoPgmyKQkTl4GVO\nnZ+GIR6DQTvGm/Fe1YAmMRlP6ea4PGQdy8lzwGFyHhkZwPLqjdg80FL0fADly045BMxes25+mrXo\nAzKrTllWGUNm9IiIaLwIsikKEYWtcLN9ZD8ANXqzbdVQxE/DEKvgqoTukcab7tWDbehXNaUdV4CB\nZpA8NwCxON4pcnjM8+2CxcDZBczQgqFVS2ehsT4DAVCfSWNCOoWbN+zG/M6tps1RSmFsvmLFaj1e\nWa8ZERFRhBjQEZVBYf3YtPYt/m56HW62x/DRnXP7+25A1hB0ZVUNtr/vBvMn2HStNN50bx5uQXvu\nGhzAJM/HFWSgGSTPXUItjrdXTRzz/LJ2i3QRMLfObsS29gVYe9mH8OfBYbzdn3PseOmVWUBmZJcZ\njHOHzcD+e0BERAQGdESh89Lm3ZHlzfZ+6/b/zW1aw5COPu1Pl2WJN700HbfkrkH3cAOGlaB7uAG3\n5K7BTS9NH7uzQ+bQbIzBz6o+hl9/6j89H1dcxxS4GtWgZ3IeWVWD1YOj16Hw/CBHSjjyEDCHmQWz\nC7wEQGN9BquWzrIsnyzrNfMg0P8eUOgYfBNREnANHVHIAh2ubLUmDkBRIAX4Xk/W25dFD1pG1nMV\niNmNtsO6tiCbY8R1TIHnczQ5jxffdwN2vjQdYvL8oLpFOlq4ongNHWAZMIeZBbNqvtJYn8G29gWO\nzw+yw2aQOGw9ObgOk4iSggEdUch6+7JYkurC8uqNmCKH0KsasHqwDU/0tTg/2cjsZtsooAYhnmaI\nuSzTC+wmKKYzAT2fo+E85gLYtsT8dQFvAXHJHR49BMxBz5nT8xuQBfpLhADFuRSUijH4JqKkYEBH\nFLKrT3oOy3PrUSsDAIAmOYTO9Hq8K10DYLG3FzPebFu1iyiUYPrIXnm6ofbTTZNc8RIs+s4suAyY\nw8yCBRGQBfpLhICEGQRTsBh8E1FSMKAjCtny9AbUDg4UbauVASxPbwDwde8vqL/ZXjsztBJMTzfU\nHsr0KHzlyiyEnQWLU0AW1Ey7uJaC0lgMvokoKRjQEYWsNnvA03ZP/JZgOsy0c31DHdN1bXFTrkHX\n5cwsxCnoCkuQa6niWgpKYzH4JqKkYEBHFLYwyxFdl2CarHFzGCBd0rGMgwCu1KCsnA0WmFkIVtAZ\nz/EQBFcCBt9ElBQM6IjCFnY5opsSTLPg0aEzJeXpspj9mcnoOnYxegbOAeAtKCtngwVmFoLFtVTj\nF4NvIkoCzqEjCpuP4d6eeZnR5qIzZaBsBo+H+lw/DPP1arNvYKWsw5JU18gubueulbsMctXSWWis\nz7ia2UZj6eePpURM92HGk4iI4oAZOqJyCLAc0bbkz8tatnJ2pvRT3hlwaainkkmTLGatDGB59cai\n+XxugrJyl0Eys1A6Y3nskBpbysyMJxERxQUDOqIEcbUOy23wWM7OlH7KOwMsDfW8js0iWzlFDhf/\n7CIoYxlk9NwG82blsQBQJYJhpbiWioiIYoUBHVGC+F6HZexqeeYVwKtPue5MWXKXRj/lnQGWhnq+\nfhZZzF41ceTvboMyNliIlpdg3irjOqwU9nV6nB1JREQUMgZ0RAniax2WWeni8w+6Xs/nq0ujn/LO\nAEtDPV8/kyzmYNUErK++CjIAz0GZsQyysE5rXAd4DqMzguIlmGeXUCIiShI2RSFKEKsbSlc3mnal\niy7Y3RA78tKsJcjnGni+fiYNbao/dRc6vvZ17OtcjG3tC0oOwAoBck9fFgqjAfKmXT0lvV4iGZrO\njKyPDKHpjZdgftmiGcikq4q2sTyWiIjiigEdUYL4utH0WbroKzvoptOnVSfLALuElnT9mtuAm18E\nOvq0PwPKHvkKkEug79o4v3NrPAJHn79k8MJLMM8uoURElCQsuSRKEF/rsHyWLnouQzMrpbv5Ret9\n7TpZ+u0Smj+W1iPdOP+kyViduwz/fnReaWWOAZUIOgXIJa9XNFHOoeaelHF0htemNEntEhrk94aI\niJJBlEk75qjNmTNH7dixI+rDIKosxqAJ0EoXS1xDB2g3xKaZC6/vZTkQfap1EOiWz/P29FpOwZ7u\n8QNowO0Dl2LzcEvRWzTmb8JdX2sX5nduNQ3GG+sz2Na+wPPrBSbMz91EpQc7nv4dJSKi2BORnUqp\nOU77MUNHNF54mVFnwlN20OuogTAzNQGOPXAsEbTLMhqCwck4iDvS64EcRoK6QsbIdzdTg3IONfek\nnKMzkNysm1tBf2+IiCgZGNARjSc+Sxdd3xB7DdDCHHIeZLBo91pOgaPJ4xkZwK01P8ITx1uKAuSb\nN+w2fZtSAzCnctnIMlc+f8kQpThm+2IbuBMRUagY0BFRaezKC70GaEFnavTHJilAjR0SXVKwaHde\nToGjxeOTcWjMbDNX6xU9rOWzWz8W+fo6v+sjIxD5NbPAcQtEROOTY5dLEblXRN4UEdMFDSLycRE5\nIiK78/+s0D12gYjsFZHfikh7kAdORBFyajfvddRAgJ0sxxybWTBXarBod15WAWJhu9PjOo7dOD22\n+7fr2hh0t81ydtOMqnNnuTuUusVxC0RE45ObDN19AP4ZwP02+/yXUuqT+g0iUgXguwDOA9ANYLuI\nbFZKvVTisRJR1EayQiZZKn15YSmldEFlasxKHwFAqgA17K+sz+m87LKMHrKQjusVS1gXaFUuG2SZ\nXjkzV1FmyeJa2uirCy4BiGcpLUWP3wuKO8eATin1jIicXsJrzwPwW6XUawAgIg8B+BQABnRESWTW\n4dFIX1YYVSmdVemjGtZmyflldV5OwZ7HINd2vWKA6wKDLNMrZ1OOKBuAxKm00exGs9TOpV5vWivt\nJjeupbQULX4vKAmCGix+jojsEZGfisgH89saAeh/jd+d32ZKRK4VkR0isuPgwYMBHRYRBcYq86UX\nRBMTvzyUNgbOOIQcKB6WDgQzpDzAc3RVpmc19N2gnJmrKLNkcSltLNxo9vRloTB6o1lK6anX1wry\nveMirqW0FC1+LygJggjo/gfAaUqpZgB3AdhUyosopdYppeYopeZMmjQpgMMiIs/sbtydsj8htpv3\nxOv6vbB4XOdm+nyrz8LNOboMwuzW13k9D6sMVRiZq3K+l5HxmtVn0piQTuHmDbsTu5bP62tV4k1u\nXEtpKVr8XlAS+O5yqZT6k+7vPxGRu0WkAUAPgKm6XZvy24gojowllcY5alYdHgGtiUlc2s3HpRW+\nn/l3Zp/FpuuAn94CZN/WzunMK4BXnzI/R6fP0sC2vNPDedh10wxaOd/LTOGaVcpaPq+vFeeb3FJL\nQeNUSkvxwe8FJYHvgE5EJgP4o1JKicg8aFm/wwD6AEwXkWnQArnLAVzh9/2IKCRON+5WTT1K7UYZ\npji0wvezzs3ssxjOAdm38q+xH3j+QetrH+QwdQ/nEURTDrc343FpAJLktXz6a50SwZBSrl/L6r0V\ngPmdW0P9LOy+I34C7Kh/SUDxxO8FJYFjQCciPwTwcQANItIN4DYAaQBQSt0D4BIAXxKRQQBZAJcr\npRSAQRG5HsCTAKoA3KuU+k0oZ0FE/jnduMcl8xU0D/PcPPEzLN1N0GcXoAU5TN3jebgePm/C6824\nn/cKStRr+Uq90TRea7Ngzu61zN67IOgspT6Aq8ukcWxgELkhZfpefgLsuPySgOKF3wtKAjddLj/j\n8Pg/QxtrYPbYTwD8pLRDI6KycnPjHofMV5A8liZ64mdYul15q55VgOYnmDQKeui7jcCzXWEF6zpR\nlmP5udE0u9YAUCWCYaUcX0v/3mbnH1SW0hh49mVztu/lN8COwy8JkqbSup2a4feC4s53ySURVYgy\n3rjHht/SRLuAwU9G0+yzMGMVoAX5WZYxMxtotivMYF0n6nKsUm80ra7psFLY17nY03tPa9+Csfm9\nYLKUVoGn1XtxvVN5saU/UTwwoCMiTaWWVNqxLE3cr3WHtLsGbgKGUjOaxs8icyowcBQYGhjdxy5A\nC/qzLFNmNtCb8SDXEdpIajlWkNc6zCDKbVBYeK+oA+zxJso1pEQ0igEdEY2qtJJKM/qsmqQAZfXb\nf2Wf1Qk7YDB+Fl7LBxP4WQZ6Mx7kOkIHcS7HsiqHC/JahxlEWQWLevr3SmqAnVRx7nZKNJ4woCOi\n+Alr7ZMxq2YZzOlYBWllDBgAJDJA8yrQm/Eg1xFGyM/6JDflcEFc6zCDKLNgMZ0SnDShGn39OdP3\ninOAXWlY4koUD6JMOltFbc6cOWrHjh1RHwYRRcEYdAHBjUdYO9P8Jl+qADUMmK4EAgABOvrcvVbd\nVODmF/0dZyn8BMFlaB5SDvrg5+qTnsPX1D2oHjo+ukNcx2xYMAZkgJaNKhr8bmN+51bTm+3G+gy2\ntS8I9FjDlJSmG0k5ziD5/Y4SkT0R2amUmuO0HzN0RBQvYZYyWmXP1LAWsFkGaSZZnTg1kfHTAKRM\nzUPCZryxvO/oPBytGcTKEx9BbfZAIgNVv+uTKqUcLgkZt/HaHIQlrkTxwICOiOIlzFJGpzI8L0Fa\nnJrI+AmCy9Q8JGxmwc/DA+fg2dqF2NaRnGyUnt+AjOVw5TOem4MkIeAmqnQM6IgoXsJc++QUsHkN\n0uKyrs1PEFzutYAhqZRslJ7fgCxJHR+TXq5Yid8/IkqOVNQHQERUZOEKLcjSC6qUsblNW0NVNxWA\naH8a11Q1t2lr4Dr6tD/jELA5sQp23QTBfp4LaCWba2dqYx7WztR+joBVkJPkbNSyRTOQSVcVbfMS\nkLXObsSqpbPQWJ+BQFs7F8e1TYVyxZ6+LBRGyxU37eqJ+tBcq8TvHxElBzN0RBQvYZcyxiWrFiQ/\n6/n8PDeA9XdBZWaSlI1yK4j1SUkoh6uEckU33z/jd/3c90/CL145mNisJBHFB7tcEhFVgii6XLrp\n9Gl47e3vuwE3vTQdvX1Z1GXSODYwiNzQ6P+H/HTIi0vZXlyOIymmtW8x7S8rAPZ1Li734ZTM7nM3\n6wZpxO6QRGTktsslAzoiIipNRz3MRz3kxzyYjKDIqhrckrsGm4dbLF82aW319djG3btKGa9gx+oc\njSrpnInIP7cBHdfQERFRaZzW35l00MzIAJZX26+zS3IjCbvywfFs064ezO/cimntWzC/c2vR+ji/\nawWTwO132mo/u+tHRMQ1dEREVBqn9XcWnTKnyGHbl01yI4mwux0msZzTaUab2VrBc98/CWue3Iub\nN+z2fZ5xuGZWHUvN9jMqZcZdHM6ZiMqHGToioriIScdI19kAs66hZ16hZeY66gEx/19Mr5po+d6J\nzMzoPrdnJ3wZS1JdY3YJIkhNajdIN1nL1tmN2Na+APs6F2PZohl4ZGdPIOcZl2tmloU0svrue836\nxuWciah8mKEjIoqDADpGBsFzNkDfNdR4DmpsA4isqsHqwdHzSacEJ02oRl9/zn8mwam5i+7x/sxk\nrM5dhn8/Os/f+xrOeTIO4o70eiCHkXWCl9T8CivlEaDjgK+urUntBuk1axnkeVq91lc2Ph9I9s8t\nqyykmy6XUV4/IkoGBnRERHFgst4Muay2vYwBnaubQavAyewcAECqADUM1DXhxffdgJ0vTYcEXQrm\nFBAbHq/NvoHl6m68lRrA5r6WsUGr286fFusEb635EZ443oKrT3oOtw79K2qyfx45rsHHb9D+5+vx\nc03q8GqvA9I9n6fNZ2X1nKF8Qzg35YtBKXWEROjXj4gSjyWXRERxYLHezHJ7SBxvBguB0ZH9ANRo\n4LRno/WxquGRQe1zl3xhpLRuW/sCbze4diWpdgGxxeO1ugYtRSVsdudoZHHOk3EI+zoXY3l6A2rU\nn4seqx46jv6fupjzZ5DU4dVem554Ok+Hz8rNtYl705pQr1/E2OyFKBgM6IiI4sCpY2SQbAIjx5tB\nu8ApzHNwCrKcAmIXDVpGglan4LBwPGtnwnxsA0bOeUL2gOnDVtvtJLUbZOvsRqxaOguN9RkItNb8\ndmMcPJ2nw2flZu0aUHr2Sh+QdHzjNvTf8f7A18CGev0ixLV+RMFhySURURw4dYwMikNp4rJFM0zn\nqI3cDNoFTkvXhXcOTiWpdU0WQ86bRv80eVzfoGUkaHUKDk3m6xXRnXPv8EQ0pQ6Nfd/hifAa5pqt\nwwpz/VeQnRLHlBvu2QisNS+T9HSeDp+V8bVSIiPllnqlZK/0602XpLqwPLcetYMD+fcPdg2sl3LN\ncn9PSsW1fkTBYUBHRBQHhZs+N+u2/HAIjBxvBu0CpzDPwSnIcgqITR7v1zVoKQpanYJDq7WCgNbp\nU3fO62uuwvLc3aiVgaL3XV9zFTpsT9hcqeuwvCqlVb5rLhoAuT3P/sxk1GbfMN+e/7v+tawGv5eS\nvdIHJMurNxZ9xgAiWQNbUK7viR9c60cUHAZ0RERxoe8YGRYXa/VsbwadAqewzsEpyHIKJg2PF7pc\nPvHneWg0Bq0lztcDBLj5xaItH1p8LVY8Noib1EOYIofRqybi27gcLYuv9X4NAuA26xZq9iTABkCr\nc5dhuRobMK/OXWYaMAeZvdIHHlNkbBYWQNnXwCaJ12YvRGSNAR0R0XjiFBg5KVcm0chNSapTMKl7\nvBZAR/4f0/0A63P0cA21QOE6XPbkwsjL37xk3ULNngTYAOjfj87DWymtuU0hYF492IYn/jzPMgPq\nJ3ulD4j15Zu9qgFNZkFdGGtgUd7B4WG9l1l5dzol6B8YxLT2LbEtFSWKI8eATkTuBfBJAG8qpWaa\nPH4lgFsACIB3AHxJKfV8/rHX89uGAAwqpeYEd+hERORZEGv1ypFJNHtPoHyBpN05eryGkZa/6Vr6\nn40GnDd0KTajZeRhq6xbqNkTv79UMBzP5r4WbB5oKdreGEKWxxgQ69firR5sQ2d6fXHZZRhrYE2O\nI8zRC2G+lzFbWpdJ49jAIN7uzwX+XkB5g2CicnPT5fI+ABfYPL4PwMeUUrMA/G8A6wyPn6uU+hCD\nOSKiCBW6Mj56LVCdATLvAiDamq+L7oxknY9nzW1aSWN+BEKQx+ypfXpzm3bN6qYi1tfQ0Bl0Mg6i\nM70eS1JdRbuZZd1C7ZS4cIUW7OiVGPyUs6OjWRkqAFSJ4InhFqxOX4f+zHsR9nfCrhw2ae/VOrtx\nZIzJiSdUIzdU3LAmqPdiR02qdI4ZOqXUMyJyus3jv9L9+GvAc+MuIiIKk7EJRfYt7QZ66bp4BiFl\nLud0lYUwOy7DerlI2F0vm9l7+ozWSNZN91qtdU1onHsDbnppevAZjQCzreXs6GhVbjqsFPZ1Lgaw\nGMDXR7Zv2tWDNZ1bAz8uq+Po6cu6LlV0m60qZ+OSMN+LHTWp0gW9hu5vAfxU97MC8LSIDAH4nlLK\nmL0bISLXArgWAE477bSAD4uIaBwLsAlFqFx0Pwz0vdyWIpbzuLxwOi4Xs/dGslkmrzX3hduwLazM\nY4Blu+UqafVShhpmqaLVcQAoyj5ZvZfTsVmtEzQeQ9DCLPNlR02qdIENFheRc6EFdLfoNrcopT4E\n4EIAfy8iH7V6vlJqnVJqjlJqzqRJk4I6LCIiCrAJRajcDPQOgtdSxHIdl1dOx2WxJu1NaRg7oDrg\nc/RUwhqlQimyi2HgXso7gyhVtLqGboal272X3bEZSxPNgjk/Ja1234swy2etgkJ21KRKEUhAJyLN\nANYD+JRSauRXf0qpnvyfbwJ4DMC8IN6PiIg8sGo2EVIHPlt2N9DlCjxtShH1XA8aj4qb2Xwma9Um\nL70d+zoXY9snDqH1l4u0z8KsSYnde9iIfL2S2yDNENiPZDgt9m+d3YhVS2ehsT4zNiA28JsRsruG\nxuOw4vUYevqy+MrG5y3XCTqds59zArxdX6/KudaSKAq+Sy5F5DQAjwL4rFLq/+q2nwggpZR6J//3\n8wFE/OtMIqJxKIjOlkFwKhEMsPuhLS+liIX3L8dxeeVnNp/xs7B7D48iXa/kpTy2hFJkt+WdfssH\nna6h/jjmd2719F52JZtmGTlAv06wdG6+F2GVz5ZzrSVRFNyMLfghgI8DaBCRbgC3AUgDgFLqHgAr\nAEwEcLeIAKPjCd4D4LH8tmoADyql/iOEcyAiIjtRzY4zcrqBLlfgaREIFUoRx9zsxSUgNvIzm8/s\nszAq8RwjXa/kJUgLMfNqNmPNS0bIyzVctmgGuh67GzfhIUyRQ+hVDdrw+kXXuT42J5Wwji3S8SEB\n4egFsuKmy+VnHB6/BsA1JttfA3Bm6YdGRESBiWJ2nJHTDXS5Ak+LQGjyRbdjX7NJFiIuAXGQx2Ub\ntIivcwx1hp0TL0FaiJlXvxkhL9ewtWobPplej+qh4wCAJjmEzqr1qK46E8DYz09/bFaZOr0g17GF\n+b2o9GCnnPMHKXlEWaTXozRnzhy1Y8eOqA+DiIiCtHamxQ301PKPAIhgPEKsjiXEz8J44wloQYF+\nPVRoN99ezsus7DSdiWymoP6aFIZs6+eyGa/hCB+fpVW5ZpUIhpUK9LNx872I42vHhdVn1Vifwbb2\nBREcEZWDiOx0M8s76LEFRERE5uJUuhiHjCUQ3UiEEj4Lt0GYU3Yq1EyDl/OKUebVeE36sjmkU4JT\na9Po68/ZB1Y+SketSkPDCITCXMdmtT7vKxufx80bdldExi7qklWKNwZ0RERUHjG6gQ6Vl4xbVDMC\nPX4WXoMwu/VKnpumeLmeXr9jUQb2DrMQc8MKtTXV2LXifPvX8VE6Wu5mIWGtY7MKagpNXiqhPDHS\nUmYzcapyIAZ0RERURnHJjIXFa8YtypEIHj6LIDtXeso0lJLBTMJ3zHBehVmIyAGbh0eDOlfZF5+Z\n70poFmLXubOgbJ1WDYIqL/bbaCdQUVUWkKXABosTERGNe14HdMdpRqCNIMu9PA15jutQd7+8zkK0\n09ymrfurmwqtoc3UyNYBRsXNsHWg/OWJZrP3lv3oecxe+ZTpcHU7Yc7p86xS/71MMGboiIiIguI1\n4xandYVGupKqZyc04PaBS4uyR0Bp5V6eMg1xHerul9dZiE68ZiXtyuUSUkpnzHxdfFYjfvHKQfT2\nZZESMZ2pV+7yRLPMdm5Y4e3+HADvpaBxyaaqI92mQ+2ttlP4GNAREVE8lfPGMqj38rqeqdzrCt2e\np0lJ4B2GksBSy708rdsKY7RAHAIWr7MQg2RXLgckopTObE3nIzt7RjJWVl0vy12e6CYjGFUpqFf6\nALrrhIlolENj9vkjGjA5gmMjji0gIqI4KmdL+SDfK2at8It4OTaLVvgHMAl/ffw7zgFHUEFT0NfT\n5PWyOAHtA3+LHaecV75OiFF+T+zGHADlHS1S4vfETQv/OMylszpOIwGwr9NkBmZMGAPkJakudKbX\no++brAMAACAASURBVFYGRvbpVzX4au4afOf2VVEdZkXi2AIiIkqucnZ/DPK9gsi4hZVB8nKeFiWB\nk3HI+cYzyIYJQWcwTa5BBn/GsuqNaOlrCbcTovFzPfMK4NWnypIp1Ac3v5vQbd5Awa6M1WWJq6cg\nysf3xM2aTj/liWE2MjETWadKl4ylo5uHW4AcsLx6I6bIYfSqiVg92Iadp5wX4VGObwzoiIgofsq5\ndiro9/LTZTHM7nFeztNNqaNV4Bl0MB5k10qHtWuhlb+Zfa7PP1iWjJwxu9I7PBFNqbHlciOfbYkl\nrp7nC/r4noTZwj/IOYnG8mKrgfGRdKr0wCyA3jzcgs0Do2tqM+kqrIr5eVQyBnRERBQ/YaydisN7\nOQkzM+nlPJ2atdgFnnFuZGJxDXrVxNG/h9EJMap5gxibXVk92DamXK7osy2xSY/n0RYevyf6rFld\nJo10lYQSGJUyosN4bCIoGghfKAM17puUgedWAXSVCIaVCv08knjNyo0BHRERxY+b7o9BlSbGqdNk\nmMGQl/N0KnW0C1BKCZDL1ajE5Br0qxqsHhx9r1DK3yIMco0Bqr5cril12Px6l/BZeB5t4eF7Ysya\n9WVzSKcEp9amiwKnIG7yvZ6H2bEVmGX34tKp0gurzrTlGJsQZMa0kjGgIyKi+HEKKOK8TsuPMLOF\nXs/TrtTRLkBZus5bgFzOIcW6a6COdKNXTcQduTbfnTsdRRjkmmVXNg+3YGfteUWZoxEllrh6LoP0\n8AsGq/b/tTXV2LXifM/HasfreZgdm15Sulja8dSZNmClZEzHIwZ0REQUT3Y3lnFep+VH2NnCoM7T\nLkDxGjiWuxwxfw0EwPZdPdj55F6I1U1qVFngAINcT3P/fFi2aAa6HrsbN+EhTJFD6FUN+DYuR8ui\n68yf4OF7EuRgeyder5ebYyj3QPMwRJVZLOdnn2QM6IiIKHnivE7LiV2QEKdsoR2nAMUYOO7ZmG+X\nb3JOEX6WtjepUWaBAwxyg86uWK1naq3ahk+m16N66DgAoEkOobNqPaqrzgRQQhZYJ8wmKEZer5fV\nsYV9nEGL6zq1cn72ScY5dERElDx2s7TCmJcVlDjPqfOqxCHlAIrPOa6fZbmPS389YXVvJkBHX/Dv\n7ZLVwO5VS2eh9ZeLQrtetu8bcdBhdmx6cTlOO0m7vnE5tnJwO4fOdBQJERFRrC1coQUFelE1MvHC\nLvPiRiHT1VGv/blnY/DH6Pa9Ae1GvaNP+7OUbBNg/lmm0sDAsWjOs6CcmcNC0HtkP6yDOUTTeVXH\nbj1TmNerdXYjVi2dhcb6DATaAPG43NAbj60+k8aptemR47x/7u+1YNfiu7xpVw/md27FtPYtmN+5\nFZt29ZT9HGw/14jF+bOPE5ZcEhFR8iSlNNHIz01vOZuHBPneTuds/CwzpwIDR4HsW97fK0jlHGdh\nFvQaxeAXFrbrmd4T7vWKc3dIy2PbsxF44jbLf2/K3cHRqqwy7uvU4vzZxwUzdERElEzNbe4yRHFi\ndXPr5qbXTXYvrAyen8yim3PWf5Y1JwJDA8X7esliBsVvFtjLZ2Eb0ItWthiDslyrdUtT6jPJzZqH\nyeHfm3JmxgrBY09fFgqjweOmXT32n2vSRFnFECEGdEREROXi56bXKdNlLNsrZAOCuKHxk1n0es5x\naXjT3KYFUXVT4Tmo8vpZWAa9U2P1C4tli2Ygk64q2jbSAdLP9apUDt/loDNjduWbdsGj7eeaJGH+\nNzDmWHJJRERULn5KRZ1KAMNs/++n/NDrOZez1NFJqWMevH4WcRpub8OxA6Them3a1YM1nVvdd040\nNNrZ/r4bcNNL02PXedE1h+9ykB0czco3l/3oeXz9id+grz9nuTKzty9r+rme+/5JWPPkXty8YXdy\nrn25R6DECLtcEhERxZX+BrewtkxfjqjvFtlRD/OGGgF0Rixnd85K6ARaymcR1My7mPDcndDkc8+q\nGtySu6Zo8HuiGmI4fJfNrlE6JThpQjX6+nOeAqn5nVsdxyeYaazPjBkwH3ZnydBGJIT538CIBNbl\nUkTuFZE3RcS056xo7hSR34rIHhH5K91jF4jI3vxj7d5OgYiIaBwzlg9l3wKUAjLvgmlJm5/1eU7K\nWU5XCaV7pXwWSVwTasPz+jCT7EpGBrC8erRcLi6dF11z+C6bdciEAG/nM2qFLNvslU85dsEspUwz\nnRL0DwyOee0w1/bZreXzLcz/Bsacm5LL+wD8M4D7LR6/EMD0/D8fBvAvAD4sIlUAvgvgPADdALaL\nyGal1Et+D5qIiKjimZUPDee0piG37Bu7f9hle6WWH8b9vcKQkBLKMJkFGEtSXVjevxHoOOx6wPwU\nOez4un6FOlTb4bvcWrUNrSesBCZ044BqwO3qUmxGy8jjuWGFt/tzAOy7YLoZcF4gAOoyaRwbGDR9\n7TC7XtoFi4VzKvnzGMf/3jlm6JRSzwB4y2aXTwG4X2l+DaBeRN4LYB6A3yqlXlNKDQB4KL8vERER\nOfHaHCTKzNZ46Szn9jwrIcvok3Ed2JJUFzrT69GUOgTThhUWWZReNdH2df0KNWPkxJCFn4yD6Eyv\nx5JUl+VTrDJlZo1NzDTWZ7CvczFOPKEauaHi8sTCa4fZ9dIpWPT1eYzjf++CaIrSCEC/4rM7v81s\n+4cDeD8iIqLKV0pzkCgyW1HOxysnr+eZ9CyjT8sWzShah7W8eiNqxWIcRXObaXYlq2qwenD0GobR\nedFNxig0Jln42nyZ6eaBFosnmQdFxsYmhQycPmjTXz+7wGrtZR8yXUMXxLV3agTj+/MYp//exWZs\ngYhcKyI7RGTHwYMHoz4cIiKiaCVlrpefGXXlEFT2MO7naSeCDKpxfdiU1GHzHfUD5g3ZlRfP+gZ2\nnnIeBFpmKYyGKL19WSxJdaGr5ka8dsIV6Kq5EUtSXeEN1dZ/Fma/sMHYMtMxj1tkylpnN2Jb+wLs\n61yM3bedjzWXnDly/Y3Xzy4LZ/zsgrz2TiMS4j7kPK6CyND1AJiq+7kpvy1tsd2UUmodgHWA1uUy\ngOMiIiJKLj8jDsopLnPjzASZPYzzedqJMIPaOrtxNAhY6yLjbMiuzAWwbUmoh4irT3oOy3PrR7KH\nTXIInen1eFe6BsBi7y9o163UrOuliTeloWidm1WWzUnR9TcwZlCNr233XD+cRl8EOcphPAkioNsM\n4HoReQhaSeURpdQbInIQwHQRmQYtkLscwBUBvB8REdH4kITyoTjNjTMKci5VnM/TTlxmc8W0YcXy\n9AbUDhaXgtbKAJanNwD4urcXcwqezT4Lo3QGky+6HfuatWAyrIYtxsDq6pOe067F4weAX4b7yyM/\ngSaZcwzoROSHAD4OoEFEugHcBi37BqXUPQB+AuATAH4LoB/A5/KPDYrI9QCeBFAF4F6l1G9COAci\nIiKKSkxv1AEEm1WL83naiUtmMaYZ59rsAU/bAZsgyyl4tr3mYnpNwsqUFb32no3AE98DstGvg3Uc\nXu9TqB1NI+QY0CmlPuPwuALw9xaP/QRawEdERESVKKY36gCCzarF+TztxCmzGMeMs8frYxy6XTRK\nwCl4tnyvqdrswbBZlYPGJYubF1YQa/vZJTyoC6LkkoiIiMazON6oA8Fn1eJ6nnacroHdmq9KNnLe\n+6FNZtO1b7D5jth2YXQKDqPM8tqVg4acxY1LVizSjqYhY0BHRERElSmpWbUg2V0DNw1TKjHgG9Oc\nRGEkqKubanuOtl0Yr3AI2KL8Ptpl4QLO4uoDOGNjl7CzYnbBYyV30GRAR0RERJUriVm1oFldA6dS\nu0qdMWjanES5Kn207cKYb2RiG7BF9X20y8ItXRdY5tBY1tiXzY3ZJ6ysmFNJZSV30IzNHDoiIiIi\nciGouXJOpXZJnr1nx0eJodMcNTS3aUFhR5/2Z1wCX6tsW12T6QxAXHRnScduVtZopigrFtD32a6k\nEnDx2SUYM3RERESUHHEqAYziWILMmjmV2lkGPvu1m++or78d42cz/Xzg1ae0nyUFKJOgwyro0b1W\na10TGufegJtemh75mjBPnNbvBZQ5dFu+OJIVC/D7bPXePX1ZTGvfgin1GVx8ViN+8crBZH12LjCg\nIyIiomQIuwTQS4AWVTlikB0JnW7yrQI+AICKtgTTywDvI/uBHd/XHfrQyKq5gsGqCag2KzE0ea25\nL9yGbSVmsCJTpvV7VmWNekVZsQC/z3bvraAFdo/s7MGqpbMqIojTY8klERERJUOYJYCFG/cj+1EU\nrFiVf0VVjhhkR0KnUruFK7QAz04uCzz2Rf/ln144fVYuBngLgEGVwrASdA83oD13DTYNzR+7Y5LL\nTo2ljEDo5aBmZY3plODU2jQEQGN9pjigCvD7bPbeRvoSzErCDB0RERElQ5jt1b1mCqIa2B30XDm7\nUjtjVkff2l+vUL4Ylyyly88gBYW//PMDIz8/a9aoIy6D2b2KKIPseTB4gN9n43tbfFuLSjPjMlLB\nLwZ0RERElAxhDsn2euMe1cDucs8y0wd8a2falGDmlWMgdakDvA161cTin83K9eI0mN2LMg8LLzkw\nCvj7rB9KPr9zq21Xy0oaNM6SSyIiIkoGsxLAoIIZuy6A5T4WOwF2JPTMTQkmUJ4spd12F8fZr2qw\nerD4mpm2r4/qc/arjJnFQmDUk8+KFQKjTbt6nJ/c3Ibts76OA5iEYSU4gEnYPuvrgXyfnbpaOnXF\nTBJm6IiIiCgZwmzs4DVTUM4h0WYNQBzmpYXCeM5eu0UGxeyzSqWBgWOj3TfPvGK0q6Why2V/ZjJW\nHLsYm4fPGXm6Zfv6cg8DD6pzahkzi3aBkWmmS3eO/ZnJ2HDsYjw88J2RhzPbq7Bqao/jc52uj1P5\nZ29fFktSXVhevRFT5BB6VQNWD7bhib6WEq9EdEQpqwrT6MyZM0ft2LEj6sMgIiKi8SROIxH0x2QW\naMahy2KUx6b/rDKnAgNHgaEB18cRy7VTQV7PMn4209q3mK5XEwD7Ohc7Hle/qkF77hpsHh4NpBrr\nM9jWvmD0OT9fmQ9QBUVrOX2cU8c3bsPy3N2oldHvTb+qwer0dej42tc9v14YRGSnUmqO434M6IiI\niIhiymrdWt3UaLJ0RnEIgoO+RlGdU0LPw2qtWlFQVmBxjt3DDWgZuHPk55Fg0CwwNSrx+vTf8X7U\nZt8Yuz3zXtTe8orn1wuD24COJZdEREREcVXKWqhyBiQBDaT2Jcj1YlHNFwSCX/dWps9m2aIZRc1F\nAJsSVotzmSKHi38urGd0MYKi1OtTmz3gaXucsSkKERERUVx5bdbidZ5eJfB6jexEOXcuyPMoo9bZ\njVi1dBYa6zPms+b0LM5F33G0KBh0EawdQAOmtW/B/M6t7hqxOBxL3K+3GQZ0RERERHHltctikgdh\nlyrITpRRzp0zOw99s5dyDW4vQevsRmxrX4B9nYuxrX2B9XpEk3McrJqA9TVXmQeDDsFVVtXg9oFL\nvXfXtDiWRHQwNcGSSyIiIqK48tplMamDsP0IshNllHPnjOdRaPaSfUvbXs7yT6/clvmafFbVC1eg\no7kNHWava9bRNN8Y5QAm4fbcpUXNVGy7a7o4llg0QioBm6IQERERVYq4N1GJuzh1FXX4LGPTqTPs\na2YRLHrqrplQbIpCRERENN54nadHxUrJ2oTVhMYm21oY5l1oRFIoNwRQ/qDOrsw3iOtg0dxlSn3G\ntLum6YD4CseAjoiIiKhSVFAZWWS8dIcMsyumTfmn52HeYYqozNdVd804jNUoAwZ0RERERJUkDqME\ngPFxMx1mdsom29r7oHkr/16TjJWloD6fMq47NJaZXnxWI37xykHzstMoR1CUGQM6IiIiIgrWeLmZ\nDjM7ZZNtnfIT82HerssNg/x8ylTma1Zm+sjOHusRCWGXgsYIxxYQERERUbDGy/iEsGeZNbdpzWw6\n+rQ/84HIskUzkElXFe1qOczbTJCfT3Ob1gClbioA0f4MoYmMXZmpKctge3/sx0B45SpDJyIXAPgO\ngCoA65VSnYbHlwG4UveaHwAwSSn1loi8DuAdAEMABt10aiEiIiKiBAsic5WEks2ImtAUMlIld7kM\nOrNoV+Yb0OdoVU5qWWZqVQoKAFAVlTV2DOhEpArAdwGcB6AbwHYR2ayUeqmwj1JqDYA1+f0vAnCz\nUuot3cucq5Q6FOiRExEREVE8+V1XZVYS+Oi1wKN/p2WAogzujAHKmVcArz5V9sCzdXZj6Q1QyrXu\nLcDSTs9dLU1n2BlUSAmmm5LLeQB+q5R6TSk1AOAhAJ+y2f8zAH4YxMERERERUQItXKFlqvS8ZK7M\nSgILU8cKQUEU5XKFAOXIfoxkeZ5/UDsvQ1lkrPn9fNwKsLTTc5mpsRTUSsjdOMvBTUDXCEAfwnfn\nt40hIrUALgDwiG6zAvC0iOwUkWtLPVAiIiIiSgi/66qcbrKjWo9XKWsDvX4+ezZqa87crD3T72tV\n8lhCENU6uxGrls5CY30GAqCxPmPdEKVAvwaxbqr5PiF04yy3oLtcXgRgm6HcskUp1SMi7wbwMxF5\nRSn1jPGJ+WDvWgA47bTTAj4sIiIiIiorP+MTbNc/5UWRWYlo5hqA4NcUuv18vJRNGve1YhdE2Zyn\nrzLTiNY7loObDF0PAH1I25TfZuZyGMotlVI9+T/fBPAYtBLOMZRS65RSc5RScyZNmuTisIiIiIio\nIpmVBBpFkVkJu6ulFbNSz3KVnXrJSpqWyhrYBVFhnmeZunFGwU1Atx3AdBGZJiI10IK2zcadRKQO\nwMcAPK7bdqKInFz4O4DzAbwYxIETERERUYUquvkGxqyBiiqzUq61Z0blLvUstWzSNlPpIogK+zwt\nxkAknWPJpVJqUESuB/AktLEF9yqlfiMiX8w/fk9+108DeEopdUz39PcAeExECu/1oFLqP4I8ASIi\nIiKqQPqSwLiMMLAZ9h2qcpZ6+imbtOyeOVULoJxEWdKaYP8/e3ceH2V57///9ZkkJIFsbEkgAQGR\nAEIQjVjFBQUFrQu01rr1VD09ak9d2t8pbe1ibc/pOVY97bd2Odb2eOymQNXiUlsUl6qtC0EgYd8h\nCRDWrGSf6/fHPUkmIRtkmczk/Xw8eGTmnmvm/szNEPLOtXVpDp1z7lXg1VbHnmh1/2ng6VbHdgIz\nulWhiIiIiAxs3ZmP19NCUUtfbTMA3Rs22d15an35PiNIV4ZcioiIiIhIqPTlUM/uDJvs7jy1UA1p\nDXM9vcqliIiIiIj0pL4c6tndYZPd6cEM1ZDWMGfOuVDXcIKcnByXm5sb6jJERERERPqn3ppX2NYc\nupj4iFkRMpyY2WrnXE5n7dRDJyIiIiISTk5mb7iTpV6ysKMeOhERERGRcPLjaW0Pi7QocH6FsAih\nHjoRERERkUjU3sIlriHweA/22Em/p1UuRURERETCSVeW8e/Njcf7i+AN0H88zbs/ACnQiYiIiIiE\nk7aW929LJG/I3TiPsLQAcM29kgMw1CnQiYiIiIiEk9b7vVlU2+0ieUPutjZAHwi9km3QHDoRERER\nkXATvN9be1sNRPKG3O31PkZyr2Q71EMnIiIiIhLOWvfYJY+J/H3j2ut9jOReyXaoh05EREREJNwF\n99gNBHMfHHi9ku1QD52IiIiIiISXgdgr2Q710ImIiIiISPgZaL2S7VAPnYiIiIiISJhSoBMRERER\nEQlTCnQiIiIiIiJhSoFOREREREQkTCnQiYiIiIiIhCkFOhERERERkTBlzrlQ13ACMzsE7Al1HW0Y\nARwOdREDlK59aOn6h46ufWjp+oeWrn/o6NqHlq5/6PSna3+ac25kZ436ZaDrr8ws1zmXE+o6BiJd\n+9DS9Q8dXfvQ0vUPLV3/0NG1Dy1d/9AJx2uvIZciIiIiIiJhSoFOREREREQkTCnQnZwnQ13AAKZr\nH1q6/qGjax9auv6hpesfOrr2oaXrHzphd+01h05ERERERCRMqYdOREREREQkTCnQiYiIiIiIhCkF\nui4wswVmtsXMtpvZN0JdT6QzszFm9paZbTSzDWZ2f+D4Q2ZWZGZrA3+uCnWtkcjMdptZfuAa5waO\nDTOz181sW+Dr0FDXGYnMLCvo873WzMrM7Mv67PceM3vKzA6a2fqgY+1+3s3sgcD/BVvMbH5oqo4M\n7Vz7R81ss5nlmdmfzCwlcHycmVUF/Rt4InSVR4Z2rn+732v02e857Vz7pUHXfbeZrQ0c12e/h3Xw\nc2bYfu/XHLpOmFkUsBW4HCgEVgE3Oec2hrSwCGZmo4BRzrmPzSwRWA0sBG4AKpxzj4W0wAhnZruB\nHOfc4aBjjwBHnXMPB36pMdQ59/VQ1TgQBL73FAHnAbejz36vMLOLgQrgt865aYFjbX7ezWwq8Cww\nCxgNrAQmOecaQlR+WGvn2l8BvOmcqzezHwIErv044JXGdtJ97Vz/h2jje40++z2rrWvf6vH/Bkqd\nc9/XZ7/ndfBz5m2E6fd+9dB1bhaw3Tm30zlXCywBrgtxTRHNObffOfdx4HY5sAnICG1VA951wG8C\nt3+D941PetdcYIdzbk+oC4lkzrl3gKOtDrf3eb8OWOKcq3HO7QK24/0fIaegrWvvnHvNOVcfuPsB\nkNnnhQ0Q7Xz226PPfg/q6NqbmeH9AvvZPi1qAOng58yw/d6vQNe5DKAg6H4hChd9JvCbqZnAh4FD\n9waG4jylYX+9xgErzWy1md0ZOJbmnNsfuH0ASAtNaQPKjbT8D12f/b7T3udd/x/0rTuAvwTdHx8Y\ncvY3M7soVEUNAG19r9Fnv+9cBBQ757YFHdNnv5e0+jkzbL/3K9BJv2VmCcDzwJedc2XA/wATgLOA\n/cB/h7C8SHahc+4s4ErgS4GhIU2cN05bY7V7kZkNAq4F/hg4pM9+iOjzHhpm9i2gHvhD4NB+YGzg\ne9P/BzxjZkmhqi+C6XtN6N1Ey1/m6bPfS9r4ObNJuH3vV6DrXBEwJuh+ZuCY9CIzi8H7R/YH59wL\nAM65Yudcg3POD/yKftbdHSmcc0WBrweBP+Fd5+LAmPPGsecHQ1fhgHAl8LFzrhj02Q+B9j7v+v+g\nD5jZbcDVwC2BH6oIDHU6Eri9GtgBTApZkRGqg+81+uz3ATOLBj4FLG08ps9+72jr50zC+Hu/Al3n\nVgFnmNn4wG/NbwReCnFNES0wfvx/gU3OuR8FHR8V1GwRsL71c6V7zGxIYIIwZjYEuALvOr8EfD7Q\n7PPAi6GpcMBo8Rtaffb7XHuf95eAG80s1szGA2cAH4WgvohlZguArwHXOueOBx0fGVgoCDObgHft\nd4amysjVwfcaffb7xjxgs3OusPGAPvs9r72fMwnj7/3RoS6gvwustHUPsAKIAp5yzm0IcVmRbjbw\nOSC/cdle4JvATWZ2Fl4X+G7grtCUF9HSgD953+uIBp5xzv3VzFYBy8zsn4E9eBO2pRcEgvTltPx8\nP6LPfu8ws2eBOcAIMysEvgs8TBufd+fcBjNbBmzEGw74pf60ylm4aefaPwDEAq8Hvg994Jy7G7gY\n+L6Z1QF+4G7nXFcX9JA2tHP957T1vUaf/Z7V1rV3zv0vJ86dBn32e0N7P2eG7fd+bVsgIiIiIiIS\npjTkUkREREREJEwp0ImIiIiIiIQpBToREREREZEwpUAnIiIiIiISphToREREREREwpQCnYiIhD0z\nqwh8HWdmN/fwa3+z1f1/9OTri4iIdIcCnYiIRJJxwEkFOjPrbE/WFoHOOXfBSdYkIiLSaxToREQk\nkjwMXGRma83sK2YWZWaPmtkqM8szs7sAzGyOmb1rZi/hbRaLmS03s9VmtsHM7gwcexiID7zeHwLH\nGnsDLfDa680s38w+G/Tab5vZc2a22cz+YIFdskVERHpaZ7+VFBERCSffAL7qnLsaIBDMSp1z55pZ\nLPB3M3st0PZsYJpzblfg/h3OuaNmFg+sMrPnnXPfMLN7nHNntXGuTwFnATOAEYHnvBN4bCZwJrAP\n+DswG3iv59+uiIgMdOqhExGRSHYF8E9mthb4EBgOnBF47KOgMAdwn5mtAz4AxgS1a8+FwLPOuQbn\nXDHwN+DcoNcudM75gbV4Q0FFRER6nHroREQkkhlwr3NuRYuDZnOAylb35wHnO+eOm9nbQFw3zlsT\ndLsB/X8rIiK9RD10IiISScqBxKD7K4AvmlkMgJlNMrMhbTwvGTgWCHOTgU8EPVbX+PxW3gU+G5in\nNxK4GPioR96FiIhIF+k3hiIiEknygIbA0MmngZ/gDXf8OLAwySFgYRvP+ytwt5ltArbgDbts9CSQ\nZ2YfO+duCTr+J+B8YB3ggK855w4EAqGIiEifMOdcqGsQERERERGRU6AhlyIiIiIiImFKgU5ERERE\nRCRMKdCJiEi/EVhgpMLMxvZkWxERkUilOXQiInLKzKwi6O5gvOX6GwL373LO/aHvqxIRERk4FOhE\nRKRHmNlu4AvOuZUdtIl2ztX3XVXhSddJRES6SkMuRUSk15jZf5jZUjN71szKgVvN7Hwz+8DMSsxs\nv5k9HrRPXLSZOTMbF7j/+8DjfzGzcjN738zGn2zbwONXmtlWMys1s5+a2d/N7LZ26m63xsDj081s\npZkdNbMDZva1oJq+Y2Y7zKzMzHLNbLSZTTQz1+oc7zWe38y+YGbvBM5zFPi2mZ1hZm8FznHYzH5n\nZslBzz/NzJab2aHA4z8xs7hAzVOC2o0ys+NmNvzU/yZFRKS/UqATEZHetgh4Bm/z7qVAPXA/MAKY\nDSwA7urg+TcD3wGGAXuBfz/ZtmaWCiwDFgfOuwuY1cHrtFtjIFStBF4GRgGTgLcDz1sMXB9onwJ8\nAaju4DzBLgA2ASOBHwIG/AeQDkwFJgTeG2YWDfwZ2I63z94YYJlzrjrwPm9tdU1WOOeOdLEOEREJ\nIwp0IiLS295zzr3snPM756qcc6uccx865+qdczvxNu6+pIPnP+ecy3XO1QF/AM46hbZXA2ud4p8f\nnwAAIABJREFUcy8GHvsxcLi9F+mkxmuBvc65nzjnapxzZc65jwKPfQH4pnNuW+D9rnXOHe348jTZ\n65z7H+dcQ+A6bXXOveGcq3XOHQzU3FjD+Xhh8+vOucpA+78HHvsNcHNgI3WAzwG/62INIiISZqJD\nXYCIiES8guA7ZjYZ+G/gHLyFVKKBDzt4/oGg28eBhFNoOzq4DuecM7PC9l6kkxrHADvaeWpHj3Wm\n9XVKBx7H6yFMxPsl7KGg8+x2zjXQinPu72ZWD1xoZseAsXi9eSIiEoHUQyciIr2t9epbvwTWAxOd\nc0nAg3jDC3vTfiCz8U6g9yqjg/Yd1VgAnN7O89p7rDJw3sFBx9JbtWl9nX6It2ro9EANt7Wq4TQz\ni2qnjt/iDbv8HN5QzJp22omISJhToBMRkb6WCJQClYHFOzqaP9dTXgHONrNrAvPP7sebq3YqNb4E\njDWze8ws1sySzKxxPt6vgf8ws9PNc5aZDcPrOTyAtyhMlJndCZzWSc2JeEGw1MzGAF8Neux94Ajw\nn2Y22MzizWx20OO/w5vLdzNeuBMRkQilQCciIn3t34DPA+V4PWFLe/uEzrli4LPAj/CC0OnAGrwe\nsJOq0TlXClwOfBooBrbSPLftUWA58AZQhjf3Ls55ewT9C/BNvLl7E+l4mCnAd/EWbinFC5HPB9VQ\njzcvcApeb91evADX+PhuIB+occ79o5PziIhIGNM+dCIiMuAEhiruA653zr0b6np6g5n9FtjpnHso\n1LWIiEjv0aIoIiIyIJjZAuADoAp4AKgDPurwSWHKzCYA1wHTQ12LiIj0Lg25FBGRgeJCYCfeSpHz\ngUWRuFiImf0XsA74T+fc3lDXIyIivUtDLkVERERERMKUeuhERERERETCVL+cQzdixAg3bty4UJch\nIiIiIiISEqtXrz7snOtoix2gm4EuMMH8J0AU8Gvn3MOtHk8Gfg+MDZzrMefc/3X2uuPGjSM3N7c7\npYmIiIiIiIQtM9vTlXanPOQysOTzz4ErganATWY2tVWzLwEbnXMzgDnAf5vZoFM9p4iIiIiIiDTr\nzhy6WcB259xO51wtsARvieRgDkg0MwMSgKNAfTfOKSIiIiIiIgHdCXQZQEHQ/cLAsWA/A6bgbd6a\nD9zvnPO39WJmdqeZ5ZpZ7qFDh7pRloiIiIiIyMDQ24uizAfWApcBpwOvm9m7zrmy1g2dc08CTwLk\n5ORoLwURkTbU1dVRWFhIdXV1qEsR6Za4uDgyMzOJiYkJdSkiImGtO4GuCBgTdD8zcCzY7cDDztvs\nbruZ7QImAx9147wiIgNWYWEhiYmJjBs3Dm80u0j4cc5x5MgRCgsLGT9+fKjLEREJa90ZcrkKOMPM\nxgcWOrkReKlVm73AXAAzSwOygJ3dOKeIyIBWXV3N8OHDFeYkrJkZw4cPV0+ziEgPOOUeOudcvZnd\nA6zA27bgKefcBjO7O/D4E8C/A0+bWT5gwNedc4d7oG4RkQFLYU4igT7HIhJyecvgje9DaSEkZ8Lc\nByH7hlBXddK6NYfOOfcq8GqrY08E3d4HXNGdc4iIiIiISCciJJz0mbxl8PJ9UFfl3S8t8O5D2F23\n3l4URUREIszu3bu5+uqrWb9+fY+/9ttvv81jjz3GK6+8wksvvcTGjRv5xje+0ePnCQcne52ffvpp\nrrjiCkaPHt1hm9zcXH72s5/1VJki0h/093DiHPgbwF8H/nrvT0N9821/XeDxemhobNON9g1Bj/uD\nHm8Ier31zzdfr0Z1VV4o7g/X7CQo0ImIRLDla4p4dMUW9pVUMTolnsXzs1g4s/UOM/3Ttddey7XX\nXhvqMrqmH/xm/Omnn2batGkdBrreUl9fT3S0fqQQCZk3vt92OHl1MVQebj/UNIWlroSo1u07CUyt\n/4SKLwZ80RAVA74o77YvBuqOt92+tLBv6+sB+u4rIhKhlq8p4oEX8qmqawCgqKSKB17IB+h2qKuv\nr+eWW27h448/5swzz+S3v/0tjz32GC+//DJVVVVccMEF/PKXv8TMePzxx3niiSeIjo5m6tSpLFmy\nhMrKSu69917Wr19PXV0dDz30ENddd12LcwT3Jt12220kJSWRm5vLgQMHeOSRR7j++usBePTRR1m2\nbBk1NTUsWrSI733ve916byetF38z3tXr/Pzzz5Obm8stt9xCfHw877//PuvXr+f++++nsrKS2NhY\n3njjDQD27dvHggUL2LFjB4sWLeKRRx4BICEhgfvvv59XXnmF+Ph4XnzxRdLS0ti9ezd33HEHhw8f\nZuTIkfzf//0fY8eO5bbbbiMuLo41a9Ywe/ZskpKS2LVrFzt37mTv3r38+Mc/5oMPPuAvf/kLGRkZ\nvPzyy9qiQKQ31FZ633faUl0CKx448XhjqPFFeyEnqvF2qz9RwfcDbaLj2m7fOjB19NpRQY/7Yjo4\nX1T77bt0vqj2r9uPp7V93ZIzT+3vIYQU6EREwtT3Xt7Axn0nbOvZZM3eEmob/C2OVdU18LXn8nj2\no71tPmfq6CS+e82ZnZ57y5Yt/O///i+zZ8/mjjvu4Be/+AX33HMPDz74IACf+9zneOWVV7jmmmt4\n+OGH2bVrF7GxsZSUlADwgx/8gMsuu4ynnnqKkpISZs2axbx58zo85/79+3nvvffYvHkz1157Lddf\nfz2vvfYa27Zt46OPPsI5x7XXXss777zDxRdf3Ol76LK/fAMO5Lf/eOEqaKhpeayuCl68B1b/pu3n\npE+HKx/u9NRdvc7XX389P/vZz3jsscfIycmhtraWz372syxdupRzzz2XsrIy4uPjAVi7di1r1qwh\nNjaWrKws7r33XsaMGUNlZSWf+MQn+MEPfsDXvvY1fvWrX/Htb3+be++9l89//vN8/vOf56mnnuK+\n++5j+fLl3lsvLOQf//gHUVFRPPTQQ+zYsYO33nqLjRs3cv755/P888/zyCOPsGjRIv785z+zcOHC\nzq+3iHTO3wC7/gbrlsKml9tvlzQavviPlqHJFwValMgbSRH8yziAmHjveJjpzrYFIiLSj7UOc50d\nPxljxoxh9uzZANx666289957vPXWW5x33nlMnz6dN998kw0bNgCQnZ3NLbfcwu9///umYXmvvfYa\nDz/8MGeddRZz5syhurqavXvbDpmNFi5ciM/nY+rUqRQXFze9zmuvvcbMmTM5++yz2bx5M9u2bev2\n+zsprcNcZ8dPwslc52Bbtmxh1KhRnHvuuQAkJSU1Xfu5c+eSnJxMXFwcU6dOZc+ePQAMGjSIq6++\nGoBzzjmH3bt3A/D+++9z8803A16AfO+995rO85nPfIaoqObfgF955ZXExMQwffp0GhoaWLBgAQDT\np09vej0ROUXOwf48WPEt+NFU+N0i2PIXmP5puGixF0aCxcTDvO9B/FCITYCYOK8HTGHOk30DXPM4\nJI8BzPt6zeNhN38O1EMnIhK2OutJm/3wmxSVVJ1wPCMlnqV3nd+tc7dect7M+Nd//Vdyc3MZM2YM\nDz30UNMeY3/+85955513ePnll/nBD35Afn4+zjmef/55srKyWrxOY1BrS2xsbNNt51zT1wceeIC7\n7rqrW++nQ531pLU7bGcM3P7nbp36ZK5zVwVfx6ioKOrrvbktMTExTecLPt6RIUOGtPnaPp+vxev5\nfL4uvZ6ItKG0CPKXecO7D270etvOuAJmfBbOmO8FNYCRk0I+lzfsZN8QEddIPXQiIhFq8fws4mNa\nzh+Ij4li8fysdp7RdXv37uX9998H4JlnnuHCCy8EYMSIEVRUVPDcc88B4Pf7KSgo4NJLL+WHP/wh\npaWlVFRUMH/+fH760582BbM1a9acUh3z58/nqaeeoqKiAoCioiIOHjzY3bd3cuY+2PZvxntg2E5X\nrzNAYmIi5eXlAGRlZbF//35WrVoFQHl5+SkHqgsuuIAlS5YA8Ic//IGLLrrolN+PiHRRdRms+T08\nfTX8+ExY+RDEJsIn/xu+uhVuegamXtcc5sALJl9ZDw+VeF8jIKhI16iHTkQkQjUufNIbq1xmZWXx\n85//nDvuuIOpU6fyxS9+kWPHjjFt2jTS09Obhvo1NDRw6623UlpainOO++67j5SUFL7zne/w5S9/\nmezsbPx+P+PHj+eVV1456TquuOIKNm3axPnnez2OCQkJ/P73vyc1NbXb77HLGn9o6oXfjHf1OgPc\ndttt3H333U2LoixdupR7772Xqqoq4uPjWbly5SnV8NOf/pTbb7+dRx99tGlRFBHpBQ11sP0NyFsK\nW16F+moYNgHmPADZn/Fui7TBGn872p/k5OS43NzcUJchItLvbNq0iSlTpoS6DJEeoc+zDHjOQdHH\nkLfE2xft+BGIHwbTPg0zboSMczTnbQAzs9XOuZzO2qmHTkRERESkLx3dBfl/9HrjjmyHqFiYfBVk\nfxYmzvOW3hfpIgU6EREREZHedvwobPiTt7hJwQfesXEXwewvw9RrIS45tPVJ2FKgExEJM865E1Y/\nFAk3/XHKh0iPq6+BrSu8nritK8BfByMnw9zvwvTPQMqYUFcoEUCBTkQkjMTFxXHkyBGGDx+uUCdh\nyznHkSNHiIuL67yxSLjx+70euLylXo9cdSkkpMF5d3mLJaVna16c9CgFOhGRMJKZmUlhYSGHDh0K\ndSki3RIXF0dmZmaoyxDpOYe2eiEufxmU7IWYwTDlGm9e3PhLvE29RXqBPlkiImEkJiaG8ePHh7oM\nEREBqDjkrU6ZtwT2rQHzwYRL4dJvw+RPQmxCqCuUAUCBTkRERESkq2qPe/vE5S319o1zDd4wyvn/\n6W03kJge6gplgFGgExERERHpiL8Bdr8L65bCppegtgKSMmH2fd6QylTtpyiho0AnIiIiItKWA+sD\n8+Keg/J9EJsEZy7yQtxps8HnC3WFIgp0IiIiIiJNyvYFNv1eBsXrwRcNEy+HBf8JkxZATHyoKxRp\nQYFORERERAa2mnLY+JLXG7frHcBB5rlw1WNw5qdgyPBQVyjSLgU6ERERERl4Gupgx1teiNv8Z6iv\ngqHj4ZKve/vFDT891BWKdIkCnYiIiIgMDM552ws0zos7fhjih8JZN8OMG71eOW36LWFGgU5ERERE\nItuxPd6G3+uWwpFtEBULWQu8xU0mXg7Rg0Jdocgp61agM7MFwE+AKODXzrmHWz2+GLgl6FxTgJHO\nuaPdOa+IiIiISIeqjsGG5V5v3N73vWOnzYYL7oWp10F8SmjrE+khpxzozCwK+DlwOVAIrDKzl5xz\nGxvbOOceBR4NtL8G+IrCnIiIiIj0ivoa2PaaF+K2roCGWhgxCS77jjcvLmVsqCsU6XHd6aGbBWx3\nzu0EMLMlwHXAxnba3wQ8243ziYiIiIi05BwUfAjrlsCGP0F1CQxJhXO/4IW4UWdpXpxEtO4Eugyg\nIOh+IXBeWw3NbDCwALinG+cTERERCV95y+CN70NpISRnwtwHvcAhp+bwdq8nLm8plOyB6HiYcjVk\n3wgT5kCUloqQgaGvPunXAH/vaLilmd0J3Akwdqy6w0VERCSC5C2Dl++DuirvfmmBdx8U6trTVgA+\n/TJY/7wX4opWg/lg/CUw5wEvzMUmhrpqkT5nzrlTe6LZ+cBDzrn5gfsPADjn/quNtn8C/uice6Yr\nr52Tk+Nyc3NPqS4RERGRfqPqGBzcDEtu8m635ouB9GlgUeCLAl+0F1KabgeOm8+774sKaht026KC\nHu9qW1+r855sW1+r87bx/M7atjcUsnUABu99OQc4SJ/urVA57XpIGtUrf3UioWZmq51zOZ21604P\n3SrgDDMbDxQBNwI3t1FIMnAJcGs3ziUiIiLSfx0/Coc2e38Obm6+XVHc8fP8dTB4OPgbwDWA3+9t\neF1fHXSsodXtenD+oGP1gdv+oNuB4/2d+doOj9Wl3vsJ5vxeD9wdr0Ha1NDUK9IPnXKgc87Vm9k9\nwAq8bQuecs5tMLO7A48/EWi6CHjNOVfZ7WpFREREQqnySCCsbYJDW+Bg4GvlweY2MUNgZBZMnOd9\nHTkZXr4fyvef+HrJY+DW53uvXr+/OQieEP4aw2FDO4GwrSDZxbadnavNtkG1rvpV2++npkJhTqSV\nbs2hc869Crza6tgTre4/DTzdnfOIiIiI9KnKw4GwttkLbI09bpWHmtsMSvAC2xlXeF9Tp3hfkzK9\nXqZgl3//xCGEMfHevLDe5PMBPoiK6d3z9LStf/XmGbaWnNn3tYj0c1r+R0RERAYm57zg1rq37dAm\nOH6kud2gREidDJPmw8gpXo9b6mRIyuj6cviNC59olcuumftgaAKwSBhSoBMREZHI5hxUHAzqbQsK\ncFVBC3DHJnlhbfInva8js7wAlzS6Z/Yxy75BAa6rFIBFukyBTkRERCKDc94iJK0XJjm0ueUKk7HJ\nXg/blGuae9tGTobEUdqAuj9RAJZetnxNEY+u2MK+kipGp8SzeH4WC2dmhLqsk6ZAJyIiIuHFOSg/\n0MZQyc1QXdLcLi7Fm9c29brAUMnAPLeENAU3kQFu+ZoiHnghn6q6BgCKSqp44IV8gLALdQp0IiIi\n0j85B2X7ThwqeWizt6x9o/ihXmCb9qmWQyUTUhXcRKRJWXUd6wtLWVtYwuNvbKO6ruXWGFV1DTy6\nYosCnYiIiMhJcQ7KitoYKrkFasqa28UP83rYpl3fcqjkkJEKbiLSQnVdA5v2l7GuoIS8QIjbeajz\nXdT2lVR12qa/UaATERGRvuGct8DFCRtwb4Ha8uZ2g0d4wS37hkCP22Tv/pARoatdRPqtBr9j28Fy\n8gpKWVdYwrrCEjbvL6fe7wAYmRjLjMxkFp2VQfaYFLIzkrn6p+9R1EZ4G50S39fld5sCnYiIiJya\nvGVtr0Lo90NZ4YkLkxzaArUVzc8fkuoNjzzrpuYNuEdOVnATkXY55yg4WuUFt0Dv2/p9pRyv9ebC\nJcZGkz0mmX+5eAIzMpOZMSaF9KQ4rFUv/uL5WS3m0AHEx0SxeH5Wn76fnmDOuVDXcIKcnByXm5sb\n6jJERESkPXnLTtwnzKK8YFd5GOqChjYlpDXPa2vagHsyDB7W93WLSFg5WF5NXkEpeYUlrC0sJb+w\nhGPH6wAYFO3jzNFJzMhMYcaYZLIzUxg/fAg+X9eGYPf3VS7NbLVzLqezduqhExERkZPTUAd/faBl\nmANwDVBxAM65vbm3bWSWgpuIdEnwoiWNIW5faTUAPoNJaYlcMTWd7DHJzMhMISs9kZgo3ymfb+HM\njH4V4E6VAp2IiIh0zjnY9zGsWwrrn4PjR9puV18LV/6wb2sTkbBTXdfAxv1l5LWzaMlpwwdzzrhh\n3BEYNnnm6CQGD1J0aYuuioiIiLSvpADyl8G6JXB4K0TFQtaVsPs9OH74xPbJmX1fo4j0a8GLlqwt\nLCGvC4uWDB0yKMRVhw8FOhEREWmpugw2veSFuN3vAQ7GXgDXfAmmLoT4lLbn0MXEewujiMiA1bho\niTds0ut9yy8qbVp8JDEumuzMxkVLvLlvbS1aIl2nQCciIiLQUA8734a8JbDpFaivgmET4NJveitX\nDh3Xsn32Dd7Xtla5FJEBoyuLlnz23DGntGiJdI0CnYiIyEB2IN/ricv/I1QUQ1wKnHUzzLgRMs/t\neMPu7BsU4EQGkL5etES6RoFORERkoCnb7wW4dUvg4AbwxcCk+ZD9We9rdGyoKxSRENOiJeFDV11E\nRGQgqK2EzX+Gdc96QyudHzJy4KrHYNqntbWAyADWuGjJuoIS1hWWtrNoSYoWLemnFOhEREQilb8B\ndr/rbTWw6SWorYDksXDRv0H2jTBiYqgrFJE+5pxj79HjXnArKGFdYQnri8q0aEkYU6ATERGJNAc3\ne4ub5C2DsiKITYIzF8GMm2Ds+eDTnBaRSLN8TRGPrtjCvpIqRqfEs3h+FgtnZjQtWrKusLn3rUSL\nlkQUc86FuoYT5OTkuNzc3FCXISIiEj4qDsH6570hlfvXgkXBxLne4iZZV3lbCohIxHHO8cfcAh58\ncQPV9f6m4z7zettKq+qb7k9KS2RGZooWLQkTZrbaOZfTWTv10ImIiISrumrY8irkLYVtr4NrgFEz\nYP5/wfTrISE11BWKSBfU1vspraqjrLqOsqq6wO36oNt1lFV59xvblFXXe49V1TXNdQvmd1Bb7/j2\nJ6do0ZIIp79VERGRcOL3Q8EHXk/chhehphQSR8MF93q9calTQl2hyIDT4HdUNAasFqHMC2KdhbXq\nOn+Hrz8oykdSfAzJ8dEkxceQMngQpw0fQlJ8NElxMfzi7R1tPq+6roEvXDShN96y9CMKdCIiIuHg\nyA5vm4G8pVCyB2KGwNRrva0Gxl8MvqhQVyjSo9qbE9YbnHMcr204MYQF7nu364NuN4eysqo6ymvq\nO3x9nxEIZDEkxcWQFB9NWlJC4HbjcS+sNR+LbrodF9Pxv+8X1+6jqKTqhOOjUzTUeiBQoBMREemv\njh+FDS94Qa5wFWAwYQ5c+k2YfDXEJoS4QJHesXxNEQ+8kN+08mJRSRUPvJAP0G6oq6lv6LA3rKyT\nHrOGNoYtBkuIjW4OXfExZKTEM3VUUlMvWXLgeFJcdPPtQFgbMiiqV1eJXDw/q8X1AoiPiWLx/Kxe\nO6f0H90KdGa2APgJEAX82jn3cBtt5gD/D4gBDjvnLunOOUVERCJafS1se80bUrl1BfjrIHUqXP59\nmP4ZSBod6gpFet2jK7a0CCcAVXUNfOtP+by15WCLXrLG2zX1HQ9bjI32tegNGzZkEONHDGnqMUtu\n0TvW3JOWFBdDYlw00f148ZDGkNtXPZrSv5xyoDOzKODnwOVAIbDKzF5yzm0MapMC/AJY4Jzba2aa\nnS0iItKac1CY6201sP55qDoGQ0bCrDu9eXHp00F7QEkEq6ptYOP+UvIKS8kvKm1z+CBAZW0D6wpK\nmoYmjkqObwpdSUE9ZG2Fss6GLYa7hTMzFOAGqO700M0CtjvndgKY2RLgOmBjUJubgRecc3sBnHMH\nu3E+ERGRyHJsj7dX3Lpn4egOiI6DyZ/09oubcClEaWaERJ7jtfVs3FdGfpEX3tYXlbL9YAWNIx5H\nJMQSG+1rs8ctIyWetxdf2scVi/Rv3fmfIgMoCLpfCJzXqs0kIMbM3gYSgZ84537b1ouZ2Z3AnQBj\nx47tRlkiIiL9WHUpbFjuLW6y5+/esXEXwYVfganXQVxSaOsT6UGdhbeRibFMz0hmwbRRTM9IZnpG\nMmlJsby4dp/mhIl0UW//6i8aOAeYC8QD75vZB865ra0bOueeBJ4Eb2PxXq5LRESk7zTUwY43vZ64\nLX+B+moYfgZc9m1vlcoU/SJTwl+L8BYYOrnjUPvhLTszmbSkuDZfS3PCRLquO4GuCBgTdD8zcCxY\nIXDEOVcJVJrZO8AM4IRAJyIiElGcg/3rvBUq1z8HlYcgfhic/U+QfSNknK15cRK2GsNbXqHX69Ze\neLty+iiyM5KZ3kF4a4/mhIl0TXcC3SrgDDMbjxfkbsSbMxfsReBnZhYNDMIbkvnjbpxTRESkfyst\ngvxlsG4pHNoEUYNg0gJvcZOJl0P0oFBXKHJSKmvq2bi/jPxOwttV0wPDJk8hvInIqTvlQOecqzez\ne4AVeNsWPOWc22Bmdwcef8I5t8nM/grkAX68rQ3W90ThIiIi/UZNBWx62RtSuesdwMGY8+DqH8OZ\niyB+aKgrFOmStsLb9kMVuKDwlq3wJtKvmHP9b7paTk6Oy83NDXUZIiIi7fM3wK6/eUMqN70Mdcch\n5TRvhcrsG2D46aGuUKRDweEtP6jnrfFHw9RAz9u0wGIlCm8ifcvMVjvncjprp/WQRURETkbxBi/E\n5f8RyvdDXLIX4Gbc5PXKaV6c9EON4a31nLfW4e2T6nkTCTsKdCIiIo3ylsEb34fSQkjOhLkPemGt\nvNhb2GTds3AgH3zRcMYVkP2wNz8uRj/4Sv9RWVPPhsBqk52Ft+xMr/ctVeFNJGxpyKWISCRrL6DI\nifKWwcv3QV1V87GoQTB8kre4iWuA0Wd7i5tM+zQMGRG6WkUCuhLesjODhk0qvImEDQ25FBEZ6FoH\nlNIC7z70/1Dn94O/3gtR/obmry1uNz7uD3ytD3q81fO70nbFAy3DHEBDrRfmZt/vBbmR2tRYQic4\nvOUXlpBfVMrOw5VN4S0tyet5uzp7lMKbyACiQCciEqne+P6JAaWuCv78VTiyo4Ow1EEwajdY+dt4\nvr/Va7V6XlNb/4nBqz9xfpj33VBXIQNMY3jLKyxp6nlrK7xdM2O0wpvIAKdAJyISiapLvR65ttSU\nwt8e9m77osGiwBcVuO3zblvgvi8qcCw66HhUy9uNbaMHtXqtKPD5Wp6jree3e96Taetrdd52amyv\nrfng6U96i5y0lpzZe39PMqAsX1PEoyu2sK+kitEp8Syen8XCmRlU1NSzIRDaOgtvjcMnUxMV3kTE\nozl0IiKR5NBW+OiXsPZZqKtsu01yJtyf74UtadbWHLqYeLjm8f4/RFX6veVrinjghXyq6pp7oKPM\nGJ4Qw6GK2hPC2/SMFKZnJim8iQxgmkMnIjJQ+P2w/XX48AnY8aa3kMe062HYeHjvRycGlLnfVZhr\nS2No0yIy0sOOVdbyvZc3tAhzAA3OUVZVz5fnTlJ4E5FTpkAnIhKuqkth7TPw0ZNwdCckpMOl34Zz\nboOEkV6boeMUUE5G9g26PtIjdh+u5PWNxby+qZjc3UfxtzMgqqbez/3zzujb4kQkoijQiYiEm8Pb\nvBC39hmorYDMWXDpt2DqdRAV07KtAopIn2jwO9YWlPD6xmJWbipm+8EKACanJ3LPpRN5dlUBh8pr\nTnje6JT4vi5VRCKMAp2ISDjw+2H7Sm9+3PaVgWGVn4ZZd0LG2aGuTmRAqqpt4L3th3l94wHe3HyQ\nwxW1RPuM8yYM49bzxjJ3Shpjhg0GYMLIhBPm0MXHRLF4vrbCEJHuUaATEenPqsuChlXBvjv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ZGZwukjhxAdpb0PRUREukuBTqSn+f2w/nl443veNgSTroTLvwcjtZeVRIaDZdXkFZYGNun2tgw4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\nIkonof+iIiJdVONkrqXth2Ldu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RqaREjTHZVjtp4SOhGRrsA52L4APvkjbJgDsUlw4vVw\n6g+9f7A7i8bJRO4X9XOLkvs0bLbSfzzEdaIP2p1AaZWPzzbuqRuFy9nn/e6O6p1Ul8BNHJpJYlzw\nA2mrkuqQ33k3SKq37CnjnRV5vLMij1U7veTuuIHpnD+mL+eP7cfAjE7wnouQD9yA1920uvQgSWBx\nC8lhK8tGmxsZXPVW06OMPTLhgj9QWu1YmVfKstwSvswtZX91ALNohvVN54QhmYwfkkl2z2QsOsYb\nkY6KDrmN8m6jYg7cVvu4bl+ElAQe6S8M2onKMQ9OCZ2ISCQLBGD9u/DJn7wP7D16wcSb4KTvRMai\n4P4ab0QxZ1HT5X59x9SXaQ6YEDHlfu3FOceavJJgApfP4q378AUcSXHRnHZMr7okri4J2b+z4bIB\noWWvKf1DGpec7CVzsZ207PAI2ba3rK4sc0Wu16Fz3IA0po7pxwVj+zEosxMkd91BG8pGXclOOs1f\nhMYJYYPkMPQ2qlGSGNzWVJLY3udc8LiXZDeWmAHnPwTRccGfWIiJr78fHXo/LrgveD8q9ogltGEr\nx+zkX7IooRMRiUS+algxHT59BPas80ZWTrsNjr868ke1DtaQo0dmwxGl7BO8+T1dSFF5Nf/e4I3C\nfby+gPwSLyEb2TeFs4Jz4SYMziDOVbe+Mc2AkyAtO4yvqvPbUVheN3L3ZY73gXd0/1TOH+sld0N6\nRX7paVdT4w+w59fD6MeBy2Lsdun8Ov1+Jg5J4+Qh6QzrlUiU83tLtARCbwPebcB34Lbax3X7Agc+\nP9DUOYPHNjhno+e3eL1A0+d0wf1NnbNuXxNxNrU0TXuKim0+4WspIWy8P6bRsQ1+vP0Bi2X5rnLe\nXr2XxTvKcFFxnDaiL5ecOJQR2ZkNj48OXutwvwyMgFFNJXQiIpGkqgSWvOB9y1qyE/qM9RqdjJrW\npgW/31qay0Oz17GzqIL+6Ynced4Ipo3vJAlAwO8tUB06ilfbMt+ivJb5oQ1XMo+JnPInvIW9V+QW\nM39dAfPW5/PljiICDlITYjgjOAJ31rBe9AnkN/wdNLl0RHD0re8Y70ORHJacfeW8u2IXb6/IY9mO\nIgCO7ZfKBWO9ssyjeieHOcLuY39lDdv3lrO9sJxtwdvthWVsLyxnZ1ElF/BvHoj9Cz2svmyz3MVx\nd813+fNvfxfGyDsZ57wk8c/jvFGmxlL6wbdneOWv/iqvesJf7f34grd120L2+xod62907CHtr/HO\n7asCOiDvqE06Yxonic0lmsGf9e/VzzMO1VyjojBQQiciEglK871lBxY965UaDTnDS+SOPqfNJYhv\nLc3l7jdXUFFT/w1uYmw0v/v62M6T1DXWYFHrhd792jKihPSQBG8CZJ8IienhjbeR/JJK/r3eG4X7\n94YC9pXXYAbjBqRz1vDefOWoZMbZJmJ2hizcXbrbe3JMojcyGfoaU/qG9wV1YblFFby7Io93V+5i\nybZ9gDdaev7Yfpw/th/HZCm5awt/wLFrf2UwaSurS9x2FJazrbCcovKaBsdnJsUxMKMHgzN7MCij\nB3/7fBtnVn7ET2Om09/2stNl8qDvcpakfo1P7zo7TK+qE4uA0SYguEZkVfOJZBOJZnllOQs37OLz\nDbvYX1ZOZoLj5EEpnJCdRFJ04CDPbyHR3LuhmSAN7ik6or+W5iihExHpzAq3wGePwrKXvH9gjr0Q\nJv0IBpzYbpeY9MDcujWXQiXHR/Od048iOT6GHvHRJMfHkBRXf79HXEzdvqS4GKLDuT5TIOD9oxs6\nfyx/Dd63vAa9RzQcxes98oiO4tX4A3yxbV9dM5PaZhy9kuM485henD+ggolxm0kpWOrFvmtlfYlU\nxlENSyf7jPa+QZYjLq+4gndX7OLdlXks3rYP52B4n+S65G54n65V/tteyqt97CisYNvesuAIW/Bn\nbzk5+yqo9td3yYyJMrJ7JjIow0vYahO3QRlJDMxIPGDZjYj8QircOvl8sLYKBBzz1ufz/Gfb2qc7\n5qEuJRIGSuhEIkUX/wMsjeR96TU6Wf2WN5n9uCu9OXK9hrXrZfZX1jDunjntcq6E2Ki6RC8pPoak\nuGjvNpjw1d0PJoYHHBOyvUdcNPExUU0vet1alfshd0mwRDGY5FUUevviU4OjXCfVj3K1cxOZ3KIK\nPl5fwPx1BXy6cQ8lVT6io4xJA+L5et/dnBq3haziL7HcxVC+13tSXPKBcSX1ate4pH3s3l/Jeyu9\nssxFWwtxDo7JSq6bcze8T3Lb3r8RxDlHQWlVg9LI2hG27YXlFATngdZKSYhpkKiFJm790hKIOcSu\nhZ26ZFzCql26Y0bAqKYSOpFIEAF/TKQdOAdb5nuJ3OaPIC4FTroRJt4Mqf3a9VKVNX5e+GwrT87f\ndEBJU63s9EQ+/ulkyqt9lFf7Ka3yUVblo6zK791We/fLq32UVtUfU17lozR4TON9ZVU+Aq385yQm\nyugRFxwNjPcSveT46PqRwbjokASyNjEMSRIbbe8RE0VU0ZaQUbyFXofN2jW0Mo9pOBKWNarZeYlN\nfYCcMqYvi7YWMj+4LtyG/FKMABNTCvlGnzxOid1E/9JVRO9ZU3/NXsMbXfNYryOdRJT8/ZXMXuUl\ndwu3FBJw3lISF4ztx9Qx/Ti2X0rEJ3dVPj85+yoOmM+2I5i0hY6QmUH/tPpRtkF1yZuXuKUlxkb8\n70MiS5u7Y3byL9WV0IlEgmaH+wfAj1Yd+XikfQX8sGYmfPonb5HnpCw45WaYcGO7z/2q8Qd4bdEO\nHp27gd37q7yOiUN68sRHm45IyZJzjipfoD4xrPaSvMYJYVl1MGkMvd9gW31iWeVrYVHjED3qkj3v\nNiOmhtG2iZH+dQyrWsPgilUk+7x5UjXRiexLH0Npr/FU9D0Bf/8JJKT35ZMNBaya83/8iFfpb3vY\n6XrxsP9yZrnT6REo48SYzVyQkcvEmI30L1tFdO3cvvg0r1S2tuwz+4TIWFpCDklBSRWzV3lLIXy+\neS8BB0N7JXH+2L5MHdOP0f1TO2Uy45yjqLymblRt+96G89ny9lcS+lEwMTaawZk9vPlsjZK27J6J\nXX9xbolI7V6O2UkooROJBPek02zHp5EXwlFfgaPP7nZrdEW8mkr48hVvjlzhJu+/32m3wXFXQWxC\nu14qEHDMXL6TP7y/nm17yzlxcE9+et4IJh6VCUR2yVKNP0B5deiIoD84Stgw8Str5pja55YG9/Ws\nzmO8bWB81EbGR21gtG0j1rxkd1sgi3zSOM42E2f1CbDPRbGHNPpYEVY7by/r2Ibz9noNj6jum9J2\ne0qrmLNqN++syGPB5r34A47BmT28OXdj+jEm+8gmdz5/gJ1FlV6iFmxAUjvitn1vOSVVvgbHZ6XE\nNxhhCy2T7JUc1ykTU5HW6kqLlSuhE+ns1syC166hyYQuNgmSMqFou/c4bRAc/RU4arKX5Onb/86p\nshgW/R98/iSU5UP/8TDpDjj2onYvt3POMXdtPg/NXsfaXSWM7JvCT6eMYPKILH0Ya0Yg4Kj01Y4W\n+ikrK8V2fUlc3mKS8pfSO/cDYuzAUcEKF0vi2T+r76yZkBqG6KWzKiyrZk6wLPOzTV5yNzAjkfPH\neA1Vxg1Iq/t/si1fsJRU1tSNqm0PzmPbERxpyy2qwB9S8xwXHcWAjERvhC2jB4My6+ezDezZg8Q4\njbJJ19eacszO/qWnEjqRzuzzJ+G9uyF9MJTuAl9l/b7aOXRjL4PCzd6cq00fwZZ/B9u3G/Q7Ljh6\nNxkGntLuoz5yiPbnwedPwOLnoLrEG1WddAcMPbNDRlY/37yXh2avY8m2fQzO7MGPvzaci8b1Jyqc\n3Si7gMA96UQ18QVLACOqk7Swls5tX1k176/ezdsr8vh04x58AUd2eiLnj+1LUnwMT8/fREVN/ZcG\noSXQgdo2/yGja6FlkvsazYnNqG3zn9HjgDltfVMT9PdAJKi5csyhmUk8Me/ITEs4XEroRDqjgB9m\n/w/850mvpPLrz8LaWa2bkOv3efOwahO8nIUQ8HlrVw0+1Ru9O3oy9Bmj8swjZc9G+OzP/P/27ju+\nyvL+//jrSggQtmxZorK0ioIIuOuqu1prEcQtWru0arXafr/9dX/bWket2hYR96hata1aqQM3WxRc\nbJQEkLACIYOM6/fHCRAUJYRzODnJ6/l45JFz7nPf1/2J3ijvXIt3H038u/jKN+CwKxOBOwXeyy/k\nDxPm8NrcArq0acYVx/ZlxJCeGTeEpL4q/v0AWpQs+/zx3N1p8eOP0lCRMllhcTn//WA5/3lvOa/P\nK6C8ctt/32rWJIvuu+WSt3rrZf6zswLd2+VuPZ+tOrT1bN+CNs3d4kLaUZ8djrkt3dvl1pu9Dg10\nUn2zsRievDQR4IZ/F772650bhle2Hha/uSXgrZyTON6yU6L3blPAa9MtGdWrprwZ8OYtiWGzTZrB\ngaPh0O8n5sqlwPwVRdz8whyem72cdi1y+O5X9+b8Q3rTPMdhU0k16zEq/vkDmlRu6TGvyG5Ok9P/\nXK9WPVPmKSwp54BffPE2Iift13XLfLbq5f67tdvxZf4l1c6Xbe0TgEW/O2XXFvQFahvotr1us6Tk\nKloBj4yE/LfhxN/D8Mt3vs1mraH/iYkvgMJ8WPjKlq/ZjyeOd+yfCHZ7HQ29D0tcpx0XI8x/KbFi\n5eLXoXlbOOIaGHY5tOqUklvmry3hTy/O5YkZeTTPyeaKY/ow5si9/M18qgwckfifYo0e8yb1bAlr\nZaa2uTl0b5dL/tqSz33WvV0ufzn3oDRUJTVebZp/8Z/Jbu1y01DRzrGHTkq1grnw0FmJUPfNcbDP\nqam/Z4yJfbg29d59/BZUlCQ2su4xdEvA6zboC/fjUrXKCnj/KXjzT/DpbGjdDQ75Hhx0QcrC8cqi\nMu6cuIAHJ38MwLnD9+C7R+9Nx1a12FNHUr309Mx8bnhydr2eryM1JpnwZ9Ihl1J9sPhNePQcyM6B\nUX9P7FWVDuWlsGTKloC37F0gJvbP2vOILQHP7RG22FgMMx+ESX9OrDbasV9iftz+I6BJ05Tccl1p\nOeNeW8jdbyyipLySsw7qwZXH9aN7Bv62UNLn1fcV9aTGpr7/mTTQSek263H453cTK1me+wTs1jvd\nFW2xYRUserU64L0ChW6PsFnxapg2Dqb8FYpXJXo0D/8h9DspZXuNlZZXcv+kxdz5ygLWFpdz8v5d\nufr4/vTp3Col95MkSfWfgU5Klxjh9Zvg5V/BHofD2Q/U72AUY2J7hAUvJ+beLXoNytaxeXuETb13\nvYYnFgBpqArzYNIdMOM+KN8AfU9IBLleh6Ss17K8sorHpi/htpfm8em6Mo7s14lrv9af/Xu0Tcn9\nJElS5jDQSelQWQ7PXg1v35/YR+70OzIvBFVWwNK3E0MzF06EvGk1tkc4dEvA6/KVhjE8c8WHiflx\nsx9PhNv9vwWHXZH4+VKkqiry71lLueWFuSxeVczgXu247sQBDN+rQ8ruKUmSMktSA10I4UTgT0A2\nMC7G+LvPfH468CugCqgAfhhjfKM2126LgU4ZqXQdPH4hLHgJjrwWjv5pwwg8Zeth8RtbAt7KuYnj\nLTtv2dx8r6Ohze7prHLHfTwpsWLl3OchpwUMPj+x2Em7Xim7ZYyRiXNWcOOEuXy4bB0Durbm2hP6\nc8yAzoSG8KxIkqSkSVqgCyFkA3OB44E8YBowKsb4QY1zWgEbYowxhDAQeCzGOKA2126LgU4ZpzAf\nHh6R6O057dZEOGioNm+PMDHxfUNB4ninAVv2vtvjMGhWD+d/VVXBvAnwxq2wZDLktodh34ahl6V8\nWOyUhau4ccIcpn+8hj06tODq4/tx2sBuZGUZ5CRJ0uclcx+6ocD8GOPC6oYfBU4HNoeyGGNRjfNb\nArG210oZb/lseGhEoidr9OPQ59gdury+r7D0OW27w6DRia+qKljx/pbeuxn3wJS/QFYO9By6JeB1\nG7Rzm6jvrIqN8N4TiaGVBR8lFn856Q8w6Fxo2jKlt34vv5AbJ8zh1bkFdG7djF+fsR9nH9yTHDcM\nliRJSVCbQNcdWFLjfR4w7LMnhRC+Afwf0BnYtL16ra6tvv4y4DKAXr1SN+RJSqr5L8JjF0CzNnDx\n89B1vx26/LN7oOSvLeGGJ2cD1O9Qt0lWFnTdP/F12BXV2yNM3hLwJv468dW8Lex55JaA136vXVNf\nWRG8fV9isZN1+dD5K/CNsbDfmYmtJFJoQUERN78wl2dnLaNdixxuOGkA5x/Sm9ymaQy2kiSpwUna\njsIxxqeAp0IIR5KYT3fcDl4/FhgLiSGXyapLSpkZ98EzV0HnfWH0Y9Cm2w43ceOEOVttaAlQUl7J\n757/KDMC3WflNE/Mq9vrq8AvqrdHeKU64L0CH/47cV67PbbMv9vzqOQPd9ywMrHtwNS7oHRtYgjo\nqbdC3+NTPq9x6doS/vTiPJ54O49mTbL4wTF9uPTIvWjTPLUBUpIkNU61CXT5QM8a73tUH9umGONr\nIYS9Qggdd/RaKSNUVSV6nV6/CfocB9+6F5q1rlNTS9eWbPP48sJSTr/9DY7q14mj+nfigB7taJKJ\nQ/RadoD9vpn4ihFWLdiyufn7TyV6zwjQ7cAtvXc9h9V9ZdA1i+Gt22HmA1BRCgNOhcN+CD0PTuZP\ntU2risq4Y+ICHpz8MQDnH7IH3zu6Dx1bZdgqp5IkKaPUZlGUJiQWNjmWRBibBpwTY3y/xjl9gAXV\ni6IMBv5NIrxlb+/abXFRFNVbFWXw9HcT87EGXwCn3FTnoXvLCks4/PcTqaz6/J/B1s2b0K9La2Z+\nsoaqCG2aN+GIvp04ql8njuzXia5tm+/sT5J+lRWQP2NLwMubBrEyseLkHoduCXid9926V23WY/DS\nLxP7xrXtAcf+DDrvk1jo5P2nIGTBAWfDoVdCp34p/zHWl5Zz1+uLuPv1hZSUV/LNwT248ri+9Nit\nRcrvLUmSGq5kb1twMnAriYA2Psb4mxDC5QAxxr+GEH4MnA+UAyXAtTW2Lfjctdu7n4FO9VLxanh0\nNHzyFhz7/+Dwq+o8fG9lURkj/jaJ/NXFEAJlFVWbP8vNyeb/ztyfMwZ1p7C4nDfmr+TVuSt4dW4B\nn64rA2BA19Yc1T8R8Ibs0Z6mTTKw9+6zStcltkfYFPBWzUscb9Wlehjn0YmFZ178GZTX6NkMWRCr\noGkrOOjCxNYDdRj+usPlllfywKSPufOV+awpLuek/bpyzdf60adz3XprJUmSanJjcSmZVi+Ch74F\naz+GM/4C+59V56YKi8sZeddkFq0s4v6Lh7F0bUmtVrmMMTLn0/W8OqeAV+cWMG3xasorIy2aZnPo\n3h05qn8nvtqvEz3bN5CeocK8LYurLHwFild98bnN2sIP34Xc3VJeVnllFY9Pz+O2l+axfF0pR/Tt\nyLUn9Gdgj3Ypv7ckSWo8DHRSsuTNSOwxV1UBox5JDAeso6KyCs67ewrv56/jrguGcFS/TnVua0NZ\nBW8tWMWrc1fwypwC8tYkeq326tiSI6vn3g3fs0PDWFWxqgo+fQ/+dsQXnBDg52tTXELkmdnLuOWF\nuSxauYFBvdpx3QkDOGTvDim9ryRJapySuQ+d1Hh9+G/4x6XQqjOc+w/o2LfOTZWWV3LpfdOZlVfI\nnaMH71SYA2jZrAnH79uF4/ftQoyRRSs38OrcRO/dI1M/4d63FtOsSRbD9uqQWFylXyf27tSSkOJV\nHlMiKwt2Hwhte0Lhks9/3rZHym4dY2TinBXcOGEuHy5bx4CurRl3/hCO3adzZv6zlCRJDYo9dNIX\nmXQnTPgJdD8IRj0KreoewDZWVHH5gzOYOGcFt4w4MOVbEpSWVzJ10erNAW/+iiIAurfL3Tz37tC9\nO9A605bSn/UY/PuKrefQ5eTCabfBwBFJv93URau5ccJHTFu8hl7tW3D18f047YBuZGcZ5CRJUmo5\n5FKqq6rKRJCb8tfEsvdn3gVN6z4vrbIqcsWjM3l21jJ++439OWdYryQWWztLVhfz2rwCXp1TwJvz\nV7JhYyVNsgIH7bHb5oC37+5tMqPHaVurXCY5zL2XX8gf/zuHV+YU0Ll1M35wbF/OHtKzYSw+I0mS\nMoKBTqqLjcXwjzEw51kY/j342q8gq+5z0KqqItf9YxZPzMjjpyfvw6VH7pXEYutmY0UVb3+yJtF7\nN6eAD5atA6BT62Yc2Tcx9+6IPh3ZrWXTNFe66y0sKOLmF+byzKxltM3N4Ttf3ZsLDundMOYhSpKk\njGKgk3ZU0Qp4+GxYOhNO+j0M+/ZONRdj5Of/ep/7Jn3Mlcf25arjU78nWl2sWFfKa/NW8urcAl6f\nV8Da4nKyAhzQs93muXcDe7Rr0MMMl64t4baX5vH4jDyaNcni4sP25NIj96JtboYNSZUkSQ2GgU7a\nEQVz4KGzoKgAzrobBpyy003+4fmPuPOVBYw5fE9+eso+GTGcsbIdJzcSAAAgAElEQVQq8m7e2s1b\nI7ybt5YYoV2LnBobm3ekc+sGsLE5sKqojDtfWcADkz+GCOcM68X3ju5Dp9bN0l2aJElq5Ax0Um0t\nfgMePQeym8I5f08sgrKT7pg4nxsnzGHU0F789hv7ZUSY25Y1Gzby+vyVmwPeyqLExub77t5m89y7\ng/bYjZzszJpbtr60nHGvL2Lc6wspKa/kzME9+OFxfemxWwPZw0+SJGU8A51UG7Meg6e/C+33hNGP\nw269d7rJe99cxM///QGnH9iNm0cc2GCGKlZVRT5cvo5XqsPd2x+voaIq0qpZEw7r04Gj+nXmyH4d\n63UoKi2v5MHJH3PHxPmsKS7npP26cvXx/ejbpXW6S5MkSdqKgU76MjHC63+El38NexwOIx+E3N12\nutnHpi/huidmcfy+Xbhz9OCM67naEetKy3lr/qrqxVVWsLSwFIA+nVttnns3dM/2NM9J/4IiFZVV\nPD4jj9temseywlKO6NuRa0/oz8Ae7dJdmiRJ0jYZ6KQvUlkOz1wFMx+A/UfA6bdDk52fM/XsrGX8\n4JG3OaxPR8ZdMIRmTdIfZHaVGCMLCoo2995NWbSajRVVNM/JYnj1xuZf7d+Z3h1a7NLhp1VVkWdn\nL+PmF+ayaOUGDuzZjutO7M+he3fcZTVIkiTVhYFO2pbSdfDY+bBwIhx5LRz9U0hCwHj5o0+57P4Z\nDOrVjvsvHtbol7kv3ljBlIVbNjZftHIDAL3at9jce3fI3h1o2axJSu4fY+SVOQXcOGEOHyxbR/8u\nrfnRCf05bp/OGTufUZIkNS4GOumzCvPhoW/Byjlw6q0w+LykNPvWgpVceM80+ndpzUOXDqNNc5e6\n/6yPV23gtepw99aCVRRvrCQnO3Bw7/aJgNe/E/27tE5K2Jq2eDV/eP4jpi1eQ8/2uVx9fD++fkD3\nBjOXUZIkNQ4GOqmmZbPg4RFQVgQj7oM+xyal2bc/WcO546bQY7dc/n7ZIY1yM+4dVVZRyYzFazb3\n3n20fD0AXdo02zw087A+HXd4D7j3lxbyxwlzmDingE6tm3HFsX05e0hPmjZpuPMYJUlSw2WgkzaZ\n9yI8fgE0b5tYybLLV5LS7PtLCxk1djLtWzblsW8fQuc2DWNvtl1tWWHJ5t671+etZH1pBdlZgUGb\nNjbv34n9urUlq7qH7emZ+dw4YQ5L15bQrV0uFx7am3fz1vLMrGW0zc3h8qP25sJDezf6Ya+SJCmz\nGegkgBn3wjNXQ5d94ZzHoE23pDQ7f0URZ/9tEs2aZPHY5YfU66X6M0lFZRXvLFm7ufduVl4hAO1b\nNuXIvh1p2Tybf8zIp7S8aqvrcrIClx21F5cdufcO9+xJkiTVR7UNdKlZkUBKt6oqePlX8MbN0Oc4\n+Na90Cw5e40tWV3MueOmEELgwTHDDHNJ1CQ7iyG92zOkd3uu+Vp/VhaV8ca8lbw6t4DX5hawasPG\nbV7XoVUzrj1hwC6uVpIkKf0MdGp4Ksrg6e/Ae/+Agy6Ek2+C7OQ86ssLSzln3GRKyiv5+7eHs1en\nVklpV9vWsVUzzhjUnTMGdaeqKrL3T55jW2MKPl1XustrkyRJqg8MdGpYilfDo6Phk7fguJ/DYT9M\nyrYEAKuKyhg9bjKrizby0KXDGdC1TVLaVe1kZQW6tcslf23J5z7r1i43DRVJkiSln8u/qeFYvQju\nPh7yp8M374bDr0pamCssKee8u6eSt6aEuy88mAN7tktKu9ox157Qn9ycrRc7yc3J5toT+qepIkmS\npPSyh04Nw5Jp8MhIiJVw/j9hj0OT1vSGsgouumcq81as567zhzB8rw5Ja1s75oxB3QG2WuXy2hP6\nbz4uSZLU2BjolPk+/Df8Ywy07gqjn4COfZPWdGl5JZfeP5138wq545xBfLV/56S1rbrZNKdOkiRJ\nDrlUJosRJt0Bfz8PuuwHl7yY1DBXXlnF9x9+m7cWrOLGswZy4n67J61tSZIkKRnsoVNmqqqE52+A\nqX+DfU6DM++CnOQtjFFZFbnq7+/w4ocr+NUZ+3Hm4B5Ja1uSJElKFgOdMs/GDYkhlnOeg0O+D8f/\nErKyt39dLVVVRW54chbPzFrGDScN4LzheyStbUmSJCmZDHTKLEUr4OGzYdk7cNKNMOyypDYfY+SX\nz3zAY9PzuOKYPnz7qL2T2r4kSZKUTAY6ZY6COfDQWbBhJZz9EAw4Oem3uOm/c7n3rcVcfNieXHV8\nv6S3L0mSJCWTgU6ZYdHr8PfRkN0MLnwWug9O+i3+8soCbp84n5EH9+R/T92HkKQ97CRJkqRUcZVL\n1X/v/h0e+Aa06gpjXkxJmLt/0mJ+//xHfP2AbvzmG/sb5iRJkpQR7KFT/RUjvPZHmPhr6H0EnP0A\n5O6W9Ns8MSOPn/3zfY7bpws3jTiA7CzDnCRJkjKDgU71U2U5PPNDmPkgDDwbvv5naNIs6bd5bvYy\nrnviXQ7r04HbzxlETrad1pIkScocBjrVP6WF8NgFsHAiHHkdHP0TSMEQyIkfreDKR2cyqNdu3HX+\nEJrnJG/rA0mSJGlXMNCpfinMg4dGwMo58PXbYfB5KbnNpAWruPzBGfTv2prxFx5Mi6b+UZAkSVLm\n8W+xqj+WzYKHRyQ2Dh/9OOx9TEpuM/OTNYy5bxq92rfg/ouH0TY3JyX3kSRJklLNQKf6Yd4L8PiF\n0LwtXPw8dPlKSm7zwdJ1XDB+Kh1bN+PBMcNo37JpSu4jSZIk7QquAKH0m34PPHw2tN8TxryUsjC3\noKCI8+6eQstmTXjwkmF0adM8JfeRJEmSdhV76JQ+VVXw8i/hjVugz/HwrXugWeuU3GrJ6mLOHTeF\nEODBMcPo2b5FSu4jSZIk7Uq16qELIZwYQpgTQpgfQrh+G5+PDiHMCiHMDiG8FUI4oMZni6uPvxNC\nmJ7M4pXBykvhyTGJMHfQRTDq0ZSFuU/XlTJ63BQ2lFVw/8XD2LtTq5TcR5IkSdrVtttDF0LIBu4A\njgfygGkhhH/FGD+ocdoi4KgY45oQwknAWGBYjc+PjjGuTGLdymTFq+HRc+CTSXDcL+CwK1OyLQHA\n6g0bOXfcFFYVlfHgmGHs261NSu4jSZIkpUNthlwOBebHGBcChBAeBU4HNge6GONbNc6fDPRIZpFq\nQFYvhIe+BWuXwFnjYb9vpuxW60rLOX/8FD5ZXcy9Fw1lUK/dUnYvSZIkKR1qM+SyO7Ckxvu86mNf\n5BLgPzXeR+DFEMKMEMJlX3RRCOGyEML0EML0goKCWpSljLNkGow7DopXwfn/TGmYK95YwcX3TGPO\n8vX89dyDOGTvDim7lyRJkpQuSV0UJYRwNIlAd3iNw4fHGPNDCJ2BF0IIH8UYX/vstTHGsSSGajJk\nyJCYzLpUD3zwL3jyUmi9O4x+Ajr2SdmtSssruez+Gbz9yRpuP2cwRw/onLJ7SZIkSelUmx66fKBn\njfc9qo9tJYQwEBgHnB5jXLXpeIwxv/r7CuApEkM41VjECJPugMfOh677w5gXUxrmyiur+P7DM3lj\n/kr+cNYBnLz/7im7lyRJkpRutemhmwb0DSHsSSLIjQTOqXlCCKEX8CRwXoxxbo3jLYGsGOP66tdf\nA36ZrOJVT816DF76JRTmQdOWsLEI9vk6nDkWcnJTdtvKqsjVj73Lix9+yq9O/wpnHeRUTkmSJDVs\n2w10McaKEML3gQlANjA+xvh+COHy6s//CvwM6ADcGRKrFVbEGIcAXYCnqo81AR6OMT6fkp9E9cOs\nx+DfV0B5SeL9xiLIagIDTk1pmIsx8tOnZvPvd5fy4xMHcN4hvVN2L0mSJKm+CDHWv+lqQ4YMidOn\nu2VdRrplPyhc8vnjbXvCVe+l5JYxRn71zIeMf3MR3z+6Dz86oX9K7iNJkiTtKiGEGdWdZF+qVhuL\nS7VWmLdjx5PglhfmMv7NRVx4aG+u+Vq/lN1HkiRJqm8MdEqu1l23fbxtauaz/e3VBdz28nxGDOnB\nz07dl5CiDcolSZKk+shAp+SJEXK3sXl3Ti4c+7Ok3+6ByR/zf//5iFMH7s7/nTmQrCzDnCRJkhoX\nA52S5/2nYMUHMPDsxJw5QuL7abfBwBFJvdWTb+fxv0+/x7EDOnPL2QeSbZiTJElSI5TUjcXViJWu\ng+dvgK4D4fQ7ITt1j9bz7y3jR4+/y6F7d+CO0YPJyfb3EpIkSWqcDHRKjom/haJPYeTDKQ1zr8xZ\nwQ8emcmBPdtx1/lDaJ6TnbJ7SZIkSfWdXRvaecvehal/gyEXQ4+DUnabyQtX8e0HZtC3c2vuuWgo\nLZv5+whJkiQ1bgY67ZyqSnjmKmjRISULn2zyzpK1XHLvNHq2b8EDlwylbW5Oyu4lSZIkZQq7OLRz\nZtwL+TPgG2Mht11KbvHhsnVcMH4q7Vs15cFLhtGhVbOU3EeSJEnKNPbQqe6KVsBLv4DeRyR9FctN\nFhYUcd7dU8jNyebhMcPp2rZ5Su4jSZIkZSIDneruv/8LG4vhlJshBRt6560p5txxU4gRHhwzjJ7t\nWyT9HpIkSVImM9Cpbha9DrMehcOuhE79kt78inWljB43haKyCu6/ZCh9OrdK+j0kSZKkTOccOu24\nio3w7DXQbg848kdJb371ho2ce/cUCtaX8eCYYXylW9uk30OSJElqCAx02nGT/gwr58A5j0NOblKb\nXldazgXjp7J4VTH3Xngwg3vtltT2JUmSpIbEIZfaMWsWw6t/gH1Og35fS2rTxRsruOTeaXy4bB1/\nPXcwh/bpmNT2JUmSpIbGQKfaixGeuw5CNpz4u6Q2XVpeybcfmMGMj9dw68gDOWZAl6S2L0mSJDVE\nBjrV3kfPwLwJcPRPoG2PpDVbXlnFDx6ZyevzVvK7bw7k1IHdkta2JEmS1JAZ6FQ7ZUXwnx9Dl/1g\n2OVJa7ayKvKjx9/lhQ8+5een7cuIIT2T1rYkSZLU0Lkoimrn1d/Bunw46x7ITs5jE2Pkf56ezT/f\nWcq1J/TnwsP2TEq7kiRJUmNhD522b/l7MOlOGHw+9BqWlCZjjPzm2Q95ZOoSvvvVvfne0X2S0q4k\nSZLUmBjo9OWqquDZqyG3HRz3i6Q1e+uL8xj3xiIuPLQ3157QP2ntSpIkSY2JQy715d55EJZMgdPv\ngBbtk9LkXa8t5E8vzeOsg3rws1P3JYSQlHYlSZKkxsYeOn2xDavghZ9Br0PhgHOS0uRDUz7mN899\nyCn7787vvzmQrCzDnCRJklRXBjp9sRd/BmXr4dSbIWvnH5WnZubxP0+/xzEDOnPL2QeSbZiTJEmS\ndoqBTtv28SSY+SAc8j3ovM9ON/f8e8v50eOzGL5nB+4cPZimTXz0JEmSpJ3l36r1eZXliYVQ2vaE\no3680829OreAHzzyNvt3b8tdFwyheU52EoqUJEmS5KIo+rzJd8KKD2Dkw9C05U41NWXhKr79wHT6\ndG7NfRcNpVUzHzlJkiQpWfzbtba2dgm88jvofzIMOKVOTTw9M58bJ8xh6doSADq2bsoDlwylbYuc\nZFYqSZIkNXoOudTWnr8+8f2k39fp8qdn5nPDk7PJX1tCBCKwvqSCN+atTFqJkiRJkhIMdNpizn/g\no2fgqOugXa86NXHjhDmUlFduday0ooobJ8xJRoWSJEmSajDQKWHjBnjuOug0AIZ/r87NbBpmWdvj\nkiRJkurOOXRKeO1GKPwELnwOmjStUxMLCooIAWL8/Gfd2uXuZIGSJEmSPsseOsGKj+CtP8OBo6H3\nYXVqYkFBEaPGTiY3J5tmn9ljLjcnm2tP6J+MSiVJkiTVYKBr7GJM7DnXtBUc/8s6NbGwOsxVVkWe\n+t5h/P6bA+neLpcAdG+Xy/+duT9nDOqe3LolSZIkOeSy0Xv3Efj4TTjtT9Cy4w5fvmjlBkbdlQhz\nD186nH5dWtOvS2sDnCRJkrQLGOgas+LV8N//gR5DYdD5O3z5opUbGDl2EuWVkUcuHU7/rq1TUKQk\nSZKkL+KQy8bspV9AyVo49WbI2rFHYfHKDYwaO5nyysjDlw4zzEmSJElpYKBrrJZMgxn3wvDvQNf9\nd+jSj1clhlmWVVTy0JhhDOjaJjU1SpIkSfpStQp0IYQTQwhzQgjzQwjXb+Pz0SGEWSGE2SGEt0II\nB9T2WqVBZQU8cxW07gZf3bF/JR+v2sDIsZMpLa/k4UuHs8/uhjlJkiQpXbYb6EII2cAdwEnAvsCo\nEMK+nzltEXBUjHF/4FfA2B24Vrva1LHw6Ww46XfQrPZDJT9ZVcyo6jD30BjDnCRJkpRutemhGwrM\njzEujDFuBB4FTq95QozxrRjjmuq3k4Eetb1Wu1hhPkz8DfQ5Hvb5eq0v+2RVMSPHTqK4Oszt280w\nJ0mSJKVbbQJdd2BJjfd51ce+yCXAf3b02hDCZSGE6SGE6QUFBbUoS3Uy4QaoqoCTb4QQanXJktXF\njLprcnWYG2aYkyRJkuqJpC6KEkI4mkSg+/GOXhtjHBtjHBJjHNKpU6dklqVN5r0IH/wTjvwRtN+z\nVpcsWV3MyLGTKSqr4MFLhvGVbm1TXKQkSZKk2qrNPnT5QM8a73tUH9tKCGEgMA44Kca4akeu1S5Q\nXgLPXQMd+sKhV9Tqkpph7qExw9ivu2FOkiRJqk9q00M3DegbQtgzhNAUGAn8q+YJIYRewJPAeTHG\nuTtyrXaR12+GNYvhlJugSbPtnr4pzK0vLTfMSZIkSfXUdnvoYowVIYTvAxOAbGB8jPH9EMLl1Z//\nFfgZ0AG4MyTmZVVUD5/c5rUp+ln0RVbOgzdugf1HwF5Hbff0vDWJOXOJMDfcMCdJkiTVUyHGmO4a\nPmfIkCFx+vTp6S6jYYgR7v86LH0XfjAdWnX+0tPz1iR65taVlPPgmGEM7NFuFxUqSZIkaZMQwowY\n45DtnVebOXTKZLOfgEWvJYZabifM5a8tYdRdkyksSQyzNMxJkiRJ9VtSV7lUPVOyFib8BLoNhoMu\n+tJT89eWMHLsJNYWl/PgJYY5SZIkKRPYQ9eQvfxrKF4Jox+HrOwvPG3p2hJGjZ3M2g3lPDBmGAf0\nNMxJkiRJmcAeuoYqfwZMGwcHXwrdDvzC05auLWHk2Mms2bCRB8YM40DDnCRJkpQxDHQNUVUlPHN1\nYs7cMT/9wtOWFSbmzK3ZsJH7LxlqmJMkSZIyjEMuG6Jpd8Oyd+Cs8dB821sOLCtM9MytKkqEuUG9\ndtvFRUqSJEnaWfbQNTTrl8PLv4K9joavnLnNU5YXljKqRpgbbJiTJEmSMpKBrqGZ8FOoKEtsU5DY\n5H0rywtLGTl2EiuLNnLfxYY5SZIkKZMZ6BqSBS/De0/A4VdBh70/9/HywlJG3TWZgvVl3HfxwRy0\nh2FOkiRJymQGuoaivBSe/RG03ysR6D7j03WJMLdiXSn3XzKUg/Zon4YiJUmSJCWTi6I0FG/+CVYv\ngHOfhJzmW3306brEnLkV60q572LDnCRJktRQGOgaglUL4PWbEoug9Dl2q49WVIe55dVhbkhvw5wk\nSZLUUDjkMtPFCM9dC9lN4YTfbvXRinWljLxrS5g72DAnSZIkNSgGukz3wdOw4CU45n+gze6bD69Y\nn5gzt7ywlHsvMsxJkiRJDZGBLpOVroP/XA9dB8LBYzYfLlhfxqixk1lWWMo9Fx7M0D0Nc5IkSVJD\n5By6TDbxt1D0KYx8GLIT/yoL1pcx6q7JLF1byr0XHcywvTqkuUhJkiRJqWIPXaZa9i5M/RsMuRh6\nHAQkwtw5d00mf00J9xjmJEmSpAbPQJeJqqrgmauhRQc49mcArCxKhLm8NSWMv/BghhvmJEmSpAbP\nIZeZ6O17IX86fGMs5LbbHOaWrCnmnguHcsjehjlJkiSpMbCHLtMUrYAXfw69j4CBI1hVVMbou6bw\nyepixl94sGFOkiRJakQMdJnmv/8LG4vhlJtZtWEj59w1hY9Xb2D8BQdz6N4d012dJEmSpF3IQJdJ\nFr0Osx6Fw65kVe4ejB43hcWrNnD3BQdzaB/DnCRJktTYGOgyRcVGePYaaLcHqw+6gtHjprBo5QbG\nX3gwhxnmJEmSpEbJRVEyxaQ/w8o5rP/mw5xz77ssWpnomTPMSZIkSY2XPXSZYM1iePUPbOx7CiNe\nbsOilRsYd8EQDu9rmJMkSZIaM3vo6rsY4bnriCGLS1acxYJVRYw7fwhH9O2U7sokSZIkpZk9dPXd\nR8/AvAmMazKKKatyGXf+EI7sZ5iTJEmSZA9d/VZWRNVz17E4uzc3rz+GuwxzkiRJkmqwh64eK33x\nt2StX8oNZRfxl/OGcpRhTpIkSVINBrp6at3imeRM+wuPVR3Dd847h6/275zukiRJkiTVMwa6eqhw\nQxl5D36HwtiS3c/6nWFOkiRJ0jYZ6OqZwuJy7rvz1+xb8SGfDvspRwzsn+6SJEmSJNVTBrp6pLCk\nnO+O+y/nFY1nbach7HPS5ekuSZIkSVI9ZqCrJwpLyjnv7imcsXIsbbNKafet2yGEdJclSZIkqR4z\n0NUDhSXlnH/3FFosn8q3sl4h69DvQed90l2WJEmSpHrOfejSbF1pOeePn8rcZauZ2vERoCcc9eN0\nlyVJkiQpA9hDl0brSss57+6pfLC0kH8eNIvWhXPhpD9A05bpLk2SJElSBqhVoAshnBhCmBNCmB9C\nuH4bnw8IIUwKIZSFEH70mc8WhxBmhxDeCSFMT1bhmW5daTnn3z2V9/MLufuMrvT78A7ofzIMODnd\npUmSJEnKENsdchlCyAbuAI4H8oBpIYR/xRg/qHHaauAK4IwvaOboGOPKnS22oVhfWs4F46fyXn4h\nd4wezJGzr0l8cNLv01uYJEmSpIxSmx66ocD8GOPCGONG4FHg9JonxBhXxBinAeUpqLFB2RTmZucV\ncvs5gzmhyUz46Bk46jpo1yvd5UmSJEnKILUJdN2BJTXe51Ufq60IvBhCmBFCuGxHimtoisoquPCe\naczKK+T2cwZxYr/W8Nx10GkADP9eusuTJEmSlGF2xSqXh8cY80MInYEXQggfxRhf++xJ1WHvMoBe\nvRpeT1VRWQUXjJ/KO0vWcvuoQZy43+7w4s+h8BO48Dlo0jTdJUqSJEnKMLXpocsHetZ436P6WK3E\nGPOrv68AniIxhHNb542NMQ6JMQ7p1KlTbZvPCEVlFVxYI8ydtP/usOIjeOvPcOBo6H1YukuUJEmS\nlIFqE+imAX1DCHuGEJoCI4F/1abxEELLEELrTa+BrwHv1bXYTFRUVsFF90xl5pK1/HlTmIsRnr0G\nmraC43+Z7hIlSZIkZajtDrmMMVaEEL4PTACygfExxvdDCJdXf/7XEEJXYDrQBqgKIfwQ2BfoCDwV\nQth0r4djjM+n5kepfzZUh7m3P1nLbSMHcfL+uyc+ePdR+PgNOO1P0LJjeouUJEmSlLFqNYcuxvgc\n8Nxnjv21xuvlJIZiftY64ICdKTBTJcLcNN7+ZC1/GnkgpwysDnPFq+G/P4UeQ2HQ+ektUpIkSVJG\nq9XG4toxG8oquOjeacz4ZA23nn0gpw7stuXDl34BJWvh1Jshy3/8kiRJkurORJFkxRsTYW764tXc\nevaBnHZAjTC3ZBrMuBeGfwe67p+2GiVJkiQ1DAa6JCremBhmOX3xam4dOWjrMFdZAc9cBa27wVev\nT1+RkiRJkhqMXbEPXaNQvLGCi++dxrTFq7nl7AP5es0wBzB1LHw6G0bcD81ap6dISZIkSQ2KPXRJ\nULKxkkvunc7URYkwd/qB3bc+oTAfJv4G+hwP+3w9PUVKkiRJanAMdDupZGMlF987jSmLVnHziG2E\nOYAJN0BVBZx8IyS2cJAkSZKknWag2wklGyu55L5EmLtpxAGcMWgbYW7ei/DBP+HIH0H7PXd9kZIk\nSZIaLOfQ1VHJxkrG3D+NSQtXcfOIA/jGoG1sw1deAs9dAx36wqFX7PoiJUmSJDVoBro6KC2v5NL7\np/PWglXc9K0vCHMAr98MaxbD+f+CJs12aY2SJEmSGj6HXO6g0vJKxtw3nTcXrOSPZx3AmYO/IMyt\nnAdv3AL7j4C9jtq1RUqSJElqFOyhq4WnZ+Zz44Q5LF1bQtMmWZRVVPHHbx3ANw/6gjAXIzx7NeS0\ngBN+s2uLlSRJktRoGOi24+mZ+dzw5GxKyisBKKuoIic70CTrS1arnP0ELHoNTrkJWnXeRZVKkiRJ\namwccrkdN06YsznMbVJeGblxwpxtX1CyFib8BLoNhoMu2gUVSpIkSWqs7KHbjqVrS3boOC//GopX\nwujHISs7hZVJkiRJauzsoduObu1ya388fwZMGwcHXwrdDkxxZZIkSZIaOwPddlx7Qn9yc7buacvN\nyebaE/pvfWJVJTxzdWLO3DE/3YUVSpIkSWqsHHK5HWcM6g6weZXLbu1yufaE/puPbzbtblj2Dpw1\nHpq3TUOlkiRJkhobA10tnDGo++cDXE3rl8PLv4K9joavnLnrCpMkSZLUqDnkMhkm/BQqyhLbFIQv\n2c5AkiRJkpLIQLezFkyE956Aw6+CDnunuxpJkiRJjYiBbmeUl8Kz10D7vRKBTpIkSZJ2IefQ7Yw3\n/wSrF8C5T0JO83RXI0mSJKmRsYeurlYtgNdvSiyC0ufYdFcjSZIkqREy0NVFjPDctZDdFE74bbqr\nkSRJktRIGejq4oOnYcFLcMz/QJvd012NJEmSpEbKQLejStfBf66HrgPh4DHprkaSJElSI+aiKDtq\n4m+h6FMY+TBk+49PkiRJUvrYQ7cjlr0LU/8GQy6GHgeluxpJkiRJjZxdTLUx6zF46RdQmAchKzHc\nUpIkSZLSzEC3PbMeg39fAeUlifexCiZcD01bwMAR6a1NkiRJUqPmkMvteemXW8LcJuUlieOSJEmS\nlEYGuu0pzNux45IkSZK0ixjotqdtjx07LkmSJEm7iIFue479GeTkbn0sJzdxXJIkSZLSyEC3PQNH\nwGm3QdueQEh8P+02F0SRJEmSlHauclkbA0cY4CRJkiTVO+oH21EAAAa0SURBVPbQSZIkSVKGMtBJ\nkiRJUoaqVaALIZwYQpgTQpgfQrh+G58PCCFMCiGUhRB+tCPXSpIkSZLqZruBLoSQDdwBnATsC4wK\nIez7mdNWA1cAf6zDtZIkSZKkOqhND91QYH6McWGMcSPwKHB6zRNijCtijNOA8h29VpIkSZJUN7UJ\ndN2BJTXe51Ufq41aXxtCuCyEMD2EML2goKCWzUuSJElS41Vvti2IMY4FxgKEEApCCB+nuaRt6Qis\nTHcRarB8vpRKPl9KJZ8vpZLPl1Ktvj5je9TmpNoEunygZ433PaqP1Uadro0xdqpl+7tUCGF6jHFI\nuutQw+TzpVTy+VIq+XwplXy+lGqZ/ozVZsjlNKBvCGHPEEJTYCTwr1q2vzPXSpIkSZK+xHZ76GKM\nFSGE7wMTgGxgfIzx/RDC5dWf/zWE0BWYDrQBqkIIPwT2jTGu29a1qfphJEmSJKkxqdUcuhjjc8Bz\nnzn21xqvl5MYTlmrazPY2HQXoAbN50up5POlVPL5Uir5fCnVMvoZCzHGdNcgSZIkSaqD2syhkyRJ\nkiTVQwY6SZIkScpQBrpaCCGcGEKYE0KYH0K4Pt31qOEIIfQMIUwMIXwQQng/hHBlumtSwxNCyA4h\nzAwhPJPuWtTwhBDahRCeCCF8FEL4MIRwSLprUsMRQriq+v+P74UQHgkhNE93TcpcIYTxIYQVIYT3\nahxrH0J4IYQwr/r7bumssS4MdNsRQsgG7gBOAvYFRoUQ9k1vVWpAKoBrYoz7AsOB7/l8KQWuBD5M\ndxFqsP4EPB9jHAAcgM+akiSE0B24AhgSY9yPxIrpI9NblTLcvcCJnzl2PfBSjLEv8FL1+4xioNu+\nocD8GOPCGONG4FHg9DTXpAYixrgsxvh29ev1JP4i1D29VakhCSH0AE4BxqW7FjU8IYS2wJHA3QAx\nxo0xxrXprUoNTBMgN4TQBGgBLE1zPcpgMcbXgNWfOXw6cF/16/uAM3ZpUUlgoNu+7sCSGu/z8C/c\nSoEQQm9gEDAlvZWogbkVuA6oSnchapD2BAqAe6qH9Y4LIbRMd1FqGGKM+cAfgU+AZUBhjPG/6a1K\nDVCXGOOy6tfLgS7pLKYuDHRSPRBCaAX8A/hhjHFduutRwxBCOBVYEWOcke5a1GA1AQYDf4kxDgI2\nkIHDlVQ/Vc9lOp3ELw66AS1DCOemtyo1ZDGxn1vG7elmoNu+fKBnjfc9qo9JSRFCyCER5h6KMT6Z\n7nrUoBwGfD2EsJjEcPFjQggPprckNTB5QF6McdPIgidIBDwpGY4DFsUYC2KM5cCTwKFprkkNz6ch\nhN0Bqr+vSHM9O8xAt33TgL4hhD1DCE1JTMb9V5prUgMRQggk5p58GGO8Od31qGGJMd4QY+wRY+xN\n4r9dL8cY/e22kibGuBxYEkLoX33oWOCDNJakhuUTYHgIoUX1/y+PxUV3lHz/Ai6ofn0B8M801lIn\nTdJdQH0XY6wIIXwfmEBidaXxMcb301yWGo7DgPOA2SGEd6qP/STG+Fwaa5KkHfED4KHqX3ouBC5K\ncz1qIGKMU0IITwBvk1gVeiYwNr1VKZOFEB4Bvgp0DCHkAf8P+B3wWAjhEuBjYET6KqybkBgqKkmS\nJEnKNA65lCRJkqQMZaCTJEmSpAxloJMkSZKkDGWgkyRJkqQMZaCTJEmSpAxloJMkNVghhMoQwjs1\nvq5PYtu9QwjvJas9SZLqwn3oJEkNWUmM8cB0FyFJUqrYQydJanRCCItDCH8IIcwOIUwNIfSpPt47\nhPByCGFWCOGlEEKv6uNdQghPhRDerf46tLqp7BDCXSGE90MI/w0h5Kbth5IkNUoGOklSQ5b7mSGX\nZ9f4rDDGuD9wO3Br9bE/A/fFGAcCDwG3VR+/DXg1xngAMBh4v/p4X+COGONXgLXAN1P880iStJUQ\nY0x3DZIkpUQIoSjG2GobxxcDx8QYF4YQcoDlMcYOIYSVwO4xxvLq48tijB1DCAVAjxhjWY02egMv\nxBj7Vr//MZATY/x16n8ySZIS7KGTJDVW8Qte74iyGq8rcW66JGkXM9BJkhqrs2t8n1T9+i1gZPXr\n0cDr1a9fAr4DEELIDiG03VVFSpL0ZfxNoiSpIcsNIbxT4/3zMcZNWxfsFkKYRaKXbVT1sR8A94QQ\nrgUKgIuqj18JjA0hXEKiJ+47wLKUVy9J0nY4h06S1OhUz6EbEmNcme5aJEnaGQ65lCRJkqQMZQ+d\nJEmSJGUoe+gkSZIkKUMZ6CRJkiQpQxnoJEmSJClDGegkSZIkKUMZ6CRJkiQpQ/1/dBdkuoirbvwA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x200719f58d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o', label='baseline')\n",
    "plt.plot(bn_solver.loss_history, 'o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.train_acc_history, '-o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(solver.val_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.val_acc_history, '-o', label='batchnorm')\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers = {}\n",
    "solvers = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "  print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "  bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "  model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "  bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  bn_solver.train()\n",
    "  bn_solvers[weight_scale] = bn_solver\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  solver.train()\n",
    "  solvers[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ozxwfvJmTSU3huH+5ZG7wY7Brm6s7+GQvl+js2hac60SSBRPhk39B\nt6Gu9dW3kkfJcYXG+KGqPDFjKbdNWsjRaU1444pDaZRki/juj/TUZLJ35pOVE+IJV7US4cR7YN3P\nrgfCGAJL4mJ8uyxFpCEQyIqPLQHfUgJrvG2luQyY5vNYgRkiMk9ErvTZnqqq67z764HUAGKpOeq3\ncotELp/pVu2PFD+MhaWfwIn3QuoBwT9/rbpuOv41c+GcNyGljUt0Hj8Apv/LDfSvDnVal82AD66G\ndkfBGeNcwm5MBRQWKf/6YBGPz1jCsD6tGHdRPxJtEd/9VlxDNeTj4sCnrupdVlfVAIElcY8C34rI\nPSJyLzAbeDiYQYhIf1wSd6vP5iNVtReuO/bvInJ0yePUtV/7bW7y1rObKyJzs7Kyghlu5Dv4CmjR\nxy36mvtn+fuH2vpF8Mm/ofNJLrZQiomBLoPgko/his+h8wmugsHjB7qu3Giu07pmHrx9ketKPud1\niLOWE1Mxu/ILufq1ebzx/Sr+dmxHHjmrB/GxtohvZaSlJgGEbtFfX751VWc9FPrrmYgXyDpxrwDD\ngA24lq8zVPXVAM6dCbT2edzK27YXEekBvAAMVdU9a0aoaqb3dSPwPq57FmCDiDT3jm0ObCwl7rGq\n2k9V+zVp0iSAcKuRmFi3dtzOLfBpmGcz5e103ZsJKXDaM+6XUFVp2cd13V73kytfoyXGkURTndZN\nS+H1M90q7ue/B3Vs8LmpmOydeVzwwvd8ungDo4d045aBXZCq/DxWU42SatM4qXZolxnxZXVVjY+A\n/gVT1V+AicBkYLuItAngsDlAZxFpLyK1gHO84/fwzjMJuFBVl/hsTxSR5OL7wInAIu/pycAI7/4I\n4MNAXkON07wHHHo1/DgBVu4zsbjqfPIvyPoNTn/OJSDhkNIG8kqZPRYN66ltWwuvnu6S8wvfh2Qb\nQWAqZm12Lmc99y0L1mzlqXP7cPER7cMdUrWSlppERqhnqPo67t8Qn+h6WyJp7LOpcuUOhBCRU3Fd\nqi1wrV5tgcVAmQObVLVARK4BpgOxwHhV/UVERnrPPwfcATQCnvH+IyzwZqKmAu972+KAN1S1eE26\nB4GJInIZsBKwUdyl6X87/DoZpvwTRn7t1lurSounwtzxbmp8x+Oq9tol1W/ldaWWULdR1cdSEbnZ\n8Now1y1+8UfQqGO4IzJR4IP5mYyZnsHa7FyaJNdmV34hqjDh0oM5rGOEv+ejUFpqMu/MXU1RkVZN\nibLExtD/NpfEZXxc8eWaTLURSEvcPcChwBJVbQ8MwM0kLZeqfqyqaaraUVXv87Y95yVwqOrlqtrA\nW0Zkz1Ii3ozWnt7tgOJjvec2q+oAVe2sqser6pYKvuaao1YiDH4UNmW45Siq0ra1rrpC855wXASs\nWTbgjn1nxIrAzk2u+kMk/jebn+vWv9u01I2Ba9Er3BGZKPDB/Exum7SQzOxcFNiYs5ttuwoYeWxH\nS+BCJL1ZMjvyCsnMzi1/52A56HKrq2oCSuLyvbFqMSISo6qfA/3KO8hEiLQT4YDT4csxVVfZoKgQ\nJl3pymsNG1/1LYD+9Bju1k/zXU/t1Kegx9mu+sM7F0PejnBH+ZfCAnj3Mlj1LZwx1i1ybEwAxkzP\nIDd/33XE3vh+VRiiqRnSqqL8VknFdVX//AO+i+AF3k1IBTKvPFtEkoAvgddFZCMQQX/tTLkGPgjL\nPoOp/4QRU0I/ueCbJ+CPr1yS1LhTaK9VET2G77uGWq/zIfVAV/1g83I49w03hi6cVOGj6yHjIzj5\nYTjwjPDGY6LK2lJag0rbbipvzwzVDTkM6FqFY1aL66p++Sj0PDewMoamWgmkJW4osBO4HlcrdTkw\nJJRBmSBLbgbH3+kSq5/fCu211syDz+9zrX+9LwjttYJBBI64Fs6bCNmrYGx/V74rnD6/D358BY66\nCQ65KryxmKjTJNn/0jMtUoK0wLbZR3KdeFqmJLCkKpYZKenEe6GoIPwrEZiwKDOJ80pnTVXVIlUt\nUNUJqvqk71IgJkr0vQRaH+LGT+wI0Y9vdw68dykkN4dT/lO1y4lUVucT4IqZbimUV051EzLC4fux\nruu7z0Vw3P+FJwYTtTbm7CK/sGif7Qnxsdx8UnoYIqo5Olf1DNViDdt7dVUnWl3VGqjMJE5VC4Ei\nEbFFqaJdTIxLrHZvg09ClBx8dJNrzTpjnEuGok3jznD5TDf+bOr1rnRZYX7VXX/RJFcXMX0wDH48\nupJgE3Y78wq47OW57Mov4oYTOtMyJQEBWqYk8MAZ3Tmtd1kFc0xlpacmszxrOwV+kuiQs7qqNVYg\nY+K2AwtF5FN8xsKp6rUhi8qERmo3OPxa+Pox6HkOdDgmeOdeMBEWvAXHjIK2hwXvvFUtIcV1rc4Y\nDbOfhKwMGD4h9GvcLf/cTQZpcyic+SLEWhkkE7iCwiL+8cZ8flm7lXEX9WNA11SuHZAW7rBqlLTU\nZPIKili5ZScdmyRV7cWL66q+d5mrq9p3RPnHmGohkDFxk4B/4yY2zPO5mWh0zC3QoL1raQrWtPQt\nv7tWq9aHwtE3B+ec4RQT634hnj4W1sxx4+TWLwzd9db+BG9f4FoCz31z36VQjCmDqjJ6yi/M/G0j\ndw89sGoH1ps90pt5M1TDMS4OfOqq3m11VWuQQMpuTfB3q4rgTAjEJ8Apj8OW5fDVo5U/X2E+TLoC\nJAaGjateLUg9z4ZLp0FRPrx4IvwaguIgm5e7cloJDeGCSZDQIPjXMNXa2C9X8Np3qxh5TEcuOLRt\nuMOpsTo1TULEzVANiz11VTfDrKCWNzcRrNwkTkR+F5EVJW9VEZwJkY793fpoXz9e+dp7sx5yrVVD\nHg//0hyh0LIvXPkFpB4AEy+Cz++HoiCNecnZAK+d4cawXDgJ6jUPznlNjTHl57U8MO03hvRswS02\ncSGs6sTH0q5RYtWuFVdS855uUtQPz7uhIKbaC6Q7tR9wkHc7CngSeC2UQZkqcOJ9UDvJrR23v0nJ\nH1/Dl49ArwtcU351ldwMRkx1a8rNeggmXgi7KzkLbddWV05r+0Y4/13XlWpMBfzw+xZunPgzB7dv\nyCNn9aiack+mTGmpSWSEqzu12IA7rK5qDRJId+pmn1umqv4HsEJt0S6pCZxwj6sIMP+Vih+/c4sb\niN+wg2vCr+7i68DQp+GkB1ytwhdPcGMB90f+LnjrfMhaDGe/Cq36BjdWU+0t27idK16ZS6uGCYy9\nsC+142LDHZLBTW74Y/NOdheEcYZocV3V5Z9BxrTwxWGqRCDdqX18bv28AvbVaOBTDdb7Amh7BHx6\nh2sRCpQqTLnWHTPsBdeiVxOIwGF/gwvec7Vhx/WHFbMqdo6iQnj/Srfw8mnPQqfjQxOrqbaycnZz\n8Us/EB8rTLjkYFLqRkBZOwO4JK6wSFmRFeaiRnvqqt5mdVWruUC6Ux/1uT0A9AGGl3mEiQ4ibu24\n/Fz4322BH/fjBFg8BQb8G1r2CV18karjcXDFZ5DYFF493S3QG0i3hapbx+nXD+Gk+/ctAWZMOXbm\nFXDZhDls3p7H+IsPonXDuuEOyfjYM0M1nOPiwKur+oDVVa0BAulO7e9zO0FVr1RVGzFZXTRJgyNv\ngEXvwtIZ5e+ftQSmjXIL4h72j1BHF7kadYTLZ0DnE2Haza5lsiCv7GNmPQxzXoAjroPD/l41cZpq\no3gtuEWZW/nvub3p0SoKF9Su5to1SiQ+VsI/Lg7cP5vFdVW3rQ13NCZEAulOvV9EUnweNxCRe0Mb\nlqlSR90AjTrDRzdA3s7S9yvY7cpq1aoLpz/vqkDUZHXqwTlvwFE3ulqnE4aU3i09dzx8cT/0PA+O\nv6tq4zRRz3ctuLtOPYDju9lacJGoVlwMHRonhb8lrlhxXdUZo8MdiQmRQP4Kn6yqe1YOVNU/gUGh\nC8lUubjaMOQ/kL3Szb4szYy73KK3Q592MzaNS2QH3AFnjod1P7uFgdf+tPc+v06Gj26EzifBqU9a\nOS1TYcVrwV11TAcuPKxduMMxZUhrlhy+teJKKq6ruuBtWPV9uKMxIRBIEhcrIrWLH4hIAlC7jP1N\nNGp3pFsqZPZ/Yf2ifZ9fOsONrTjoCkg/uerji3QHDoNL/+fujx8IH98Ejx8Io1PckiQpbeGsl91Y\nFWMqoHgtuFN6NOfWk7qEOxxTjrSmSazeksuO3QXhDsXZq65qGOq6mpAKJIl7HZgpIpeJyGXAp4BV\nbKiOTrzH1Q6dct3eRZS3b4QPRkLTbm4f41+LXnDl51CvBfwwDrauBrwJDznr4bepYQ3PRJ89a8G1\na8gjZ/W0teCiQJo3uWHZxkquJRkstRLhhLth3U/wky3xWt0EMrHhIeBeoKt3u0dVraZHdVS3oVsH\nLXOuG8MF7j+3D66G3Tkw7EWr61mepKZQuHvf7QW5rqahMQFanuWzFtxFfakTb2vBRYP0VJfERUyX\nKkD3M6FhR/cP+ugU10uwYGK4ozJBEMjEhvbAF6p6k6reBHwpIu0CObmIDBSRDBFZJiKj/Dx/vogs\nEJGFIjJbRHp621uLyOci8quI/CIi1/kcM1pEMkXkJ+9m4/OCqcdwN/N0+r/g0a5wdwNYNgMOOB1S\nu4U7uuiwNbOU7WuqNg4TtWwtuOjVumFd6sTHsCQSZqgWW/gObFsDWgSo6yWYcq0lctVAIN2p7wC+\nHemF3rYyiUgs8DRwMtANOFdESmYBvwPHqGp34B5grLe9ALhRVbsBhwJ/L3Hs46ray7t9HMBrMIES\ncQvQFu6GHJ9p6b9+YB/4QNVvVbHtxvjYmVfA5RPmkJWzmxdH2Fpw0SY2RujcNIImN4DrBSgo0UOQ\nb70D1UEgSVycqu5ZAMu7H8i/hQcDy1R1hXfMW8BQ3x1UdbY32xXgO6CVt32dqv7o3c8BFgMtA7im\nCYbvn993m33gAzfgjn27neMT3HZjylBYpFz75nwWZm7lv+f2oWdrWwsuGqWlJkfOMiNQei+A9Q5E\nvUCSuCwRObX4gYgMBTYFcFxLYLXP4zWUnYhdBuxT6M3ruu0N+M6P/ofXDTteRBr4O5mIXCkic0Vk\nblZWVgDhmj3sA185PYbDkCehfmtA3NchT1qFBlMmVWX05F+YsXgjo089gBNsLbiold4siQ3bdrN1\nZ364Q3Gsd6DaCiSJGwncLiKrRGQ1cCtwVTCDEJH+uCTu1hLbk4D3gH+q6jZv87NAB6AXsA5XDmwf\nqjpWVfupar8mTZoEM9zqzz7wlddjOFy/CEZnu6+WwJlyjPtqBa9+t5Krju7ARbYWXFTr7E1uWLIx\nQlrj/PUOAHQ9dd9tJqoEMjt1uaoeihvX1lVVD1fVZQGcOxNo7fO4lbdtLyLSA3gBGKqqm322x+MS\nuNdVdZJPPBtUtVBVi4BxuG5bE0zWHWhMlZq6YC33f/wbg3s059aBthZctNszQzVSJjfs0zvQCuq1\nhp/fhG3rwh2dqYS4QHYSkcHAAUAd8VabV9XyBkjNATp7s1szgXOA80qctw0wCbhQVZf4bBfgRWCx\nqj5W4pjmqlr8rjsd8LMyramU4lajmXe7LtT6rVwCZ61JxgTdD79v4Ya3f+agdg141NaCqxaa169D\ncu24yBoX12P43r/DNy2F54+G96+CCz+wMopRqtwkTkSeA+oC/XEtZmcCP5R3nKoWiMg1wHQgFhiv\nqr+IyEjv+eeAO4BGwDNecligqv2AI4ALgYUiUlzD6HZvJurDItILt4rqHwS5a9d4Sn7gjTFB57sW\n3LiL+tlacNWEiLjyW5HSEudP484w8EG31MjsJ+HIf4Y7IrMfAmmJO1xVe4jIAlW9S0Qexc8EBH+8\npOvjEtue87l/OXC5n+O+Bvz+O6qqFwZybWOMiWTFa8HFxQgvX2xrwVU3aanJ/G/ROlQVidR6yX0u\nguUz4bN7oP1R0LJvuCMyFRRI+2mu93WniLQA8oHmoQvJGGOqt73Wgrv4INo0srXgqpv01CT+3JlP\n1nY/FVwihQgMeQKSmsF7l7vKPCaqBNISN1VEUoAxwI+4bsxxIY3KGGOqmQ/mZzJmegZrs3OpHRfD\nroIixl3Uj162Fly1VFxDdcn67TRNrhPmaMqQ0ACGjYOXB8O0W+G0Z8IdkamAQGan3qOq2ar6HtAW\n6KKqNk3RGGMC9MH8TG6btJDM7FwU2FVQRHyssGN3QbhDMyGSVrzMSCRNbihN28PhqJvgp9dh4bvh\njsZUQIWmo6jqblXdGqpgjDGmOhozPYPc/MK9tuUXKmOmZ4QpIhNqjZNq0yixVnQkcQDH3AqtDoap\n18OfK8MdjQmQzSk2xpgQW5udW6HtpnpIS42wGqpliY1z3argxscVWitxNLAkzhhjQqxegv/hxy1S\n/Kyib6qN9GbJLFmfg6qGO5TANGgHpzwOa36ALx8OdzQmAKVObBCRPmUdWFyg3hhjTOle+fYPtuYW\nECNQ5PO3PCE+lptPSg9bXCb00lKT2ZFXSGZ2Lq0aRMkM5O5nwrKZ8OUY6HCsGy9nIlZZs1P91iT1\nKHBckGMxxphq5Y3vV3HHh79wQrdUBh6QymOfLmVtdi4tUhK4+aR0TuvdMtwhmhBKb5YEuMkNUZPE\nAQx6GFZ/B+9dAVd/7WawmohUahKnqv2rMhBjjKlOJs5Zze3vL6R/ehOeOq83teNiGda3dfkHmmqj\nU9PiGarbOa5LapijqYDayTDsBXjxRJhyHZw1wa0pZyJOoLVTDwS6AXsWu1HVV0IVlDHGRLP35q3h\n1kkLOKpzY569oC+146ycVk1UPyGe5vXrsCSSy2+VpmVfOO7/YMZomP+qq+5gIk65ExtE5E7gv96t\nP/AwcGqI4zLGmKj04U+Z3PzuzxzesZHVQzXRNUO1pMOvg/ZHu0WANy0NdzTGj0Bmp54JDADWq+ol\nQE+gfkijMsaYKPTRgnXcMPFnDmrXkBcuOsgSOEN6s2SWbtxOYVGUzFD1FRMDpz8PcXXg3UuhIIJL\niNVQAdVOVdUioEBE6gEbARvYYYwxPqb/sp7r3ppP79YpjL/4IBJqWQJnXEtcXkERKzfvCHco+6de\nCxj6FKxfADPvDnc0poRAkri5Xu3UccA8XP3Ub0MalTHGRJGZizdwzRs/0r1VfV665CASawc03NjU\nAOnRVH6rNF0Gw0GXw7dPueVHTMQIpHbq37zaqc8BJwAjvG5VY4yp8b7I2MjVr/1I1+b1mHDpwSTX\niQ93SCaCdGqahAhkrN8e7lAq58R7oUkXeH8kbM8KdzTGE8jEhskicp6IJKrqH6q6oCoCM8aYSPfV\n0iyufHUenVOTePXSQ6hnCZwpIaFWLG0a1mXJxihuiQOIT4Azx8OurfDh3yBaqlBUc4F0pz4KHAn8\nKsLk7woAACAASURBVCLvisiZIlKnvIOMMaY6m718E5dPmEuHxom8dtkh1K9rCZzxLy01OTqXGSkp\n9QDXIrf0E/hhbLijMQTWnTpLVf8GdACeB4bjJjcYY0yN9MPvW7js5bm0bVSX1y8/hAaJtcIdkolg\n6anJ/L5pB7sLCsMdSuUdfAWkDYRP/g3rF4U7mhovkJY4RCQBGAaMBA4CJgR43EARyRCRZSIyys/z\n54vIAhFZKCKzRaRneceKSEMR+VRElnpfrR6IMabKzFu5hUte+oEWKXV4/fJDaZRUO9whmQiX1iyZ\ngiLl901ROkPVlwgMfRoSUuC9yyBvZ7gjqtECGRM3EViMq5X6FNBRVf8RwHGxwNPAybhqD+eKSLcS\nu/0OHKOq3YF7gLEBHDsKmKmqnYGZ3mNjTDXywfxMjnjwM9qP+ogjHvyMD+ZnhjskAH5anc2I8XNo\nWq8Ob1xxKE2SLYEz5SueoZpRHbpUARIbw+nPQdZv8Mm/wh1NjRZIS9yLuMRtpKp+7q0ZF4iDgWWq\nukJV84C3gKG+O6jqbFX903v4HdAqgGOH8ldL4ATgtADjMcZEgQ/mZ3LbpIVkZueiQGZ2LrdNWhj2\nRG7hmq1c+OL3NEysxRtXHEJqPRsabALTvnEicTES3cuMlNTxODj8Wpg7HhZPDXc0NVYgY+Kmq+r+\ndOS3BFb7PF7jbSvNZcC0AI5NVdV13v31gN+qwiJypYjMFZG5WVk2HdqYaDFmega5+Xv/ysnNL2TM\n9IwwRQS/rN3KBS9+T7068bxxxSE0r58QtlhM9KkVF0P7xoks2RDly4yUdNy/oXlPmHwNbFsb7mhq\npIDGxIWaiPTHJXG3VuQ4VVXA7zxnVR2rqv1UtV+TJk2CEKUxpiqszc71uz0zO5dd+VU/MPy39du4\n4IXvSawVy1tXHkqrBnWrPAYT/dKaJVevljiAuFowbDwU5MGkK6GoGkzciDKhTOIy2bs8Vytv215E\npAfwAjBUVTcHcOwGEWnuHdscmylrTLXSIqX0Vq6+93zKtW/OZ9rCdeTmhf4PxtINOZw/7ntqxcXw\nxhWH0rqhJXBm/6SnJrNqy0525hWEO5TgatwJBj0Mf3wF3zwR7mhqnEAmNuxTY8PfNj/mAJ1FpL2I\n1ALOASaXOE8b+H/27ju8yvJ84Pj3ziAJkAEJM2EKBEISCFOGgAzBhSg4cZXWUYvUaqH6syK1pdJi\n67YqirZV6wBkCMoQJKIoK8gIeydAwgokkEDG8/vjfYOHcLJzck5y7s91nSvnvON5n3fk5M4zmQPc\nY4zZWcZ95wP32e/vA+aVIS9KqRpi4vBofH3kkmWB/j48OKANN3Zpzre7jvHrDzfQ7c9LeeTD9cz/\n6TBZ56v+D+OeY1nc9c6P+PgIHz1wJa0j6lX5MZT36NAkGGNgd3otq1IF6DoWOt8MK6ZCynp358ar\nFDvBnz2gb10gwh7Go/BbNYSS27YBYIzJE5HxwGLAF5hpjNkqIg/b698EJgPhwBsiApBnV4E63ddO\nehrwqYj8EjiANW6dUqqWuKlrc577Yitnz+dzIa+A5mFBTBwezagE62vnL6NiWbPvJIu2HOGrLWks\n2nyUOn4+DOzQiGtjmzKkUxNCgyo38O7+42e5a8YPFBQYPn7wSq5oVL8qTk15seimP/dQjY8Kc3Nu\nqpgI3PASpKyD2ePg4VUQEOzuXHkFMcVMnSEivwUeA5pjVWUWBnFngBnGmNeqJYdVoEePHmbdunXu\nzoZSqgx2pmVyzYuJTL05lrG9W5W4bX6BYf2BUyzafISvthzl6Jkc/H2Ffu0iuC62GcNimpR7IN5D\nJ89x+1uryc7N5+MH+1z846tUZeQXGGImf8W9fVrx9PVFR9uqJQ6shvevg/jbrSFIVIWJyHpjTI/S\ntiu2JM4Y8zLwsog8aox5tUpzp5RSxVianAbA0E5OO55fwtdH6NWmIb3aNGTyDTFsTMngqy1HWbT5\nCJNmb8L3c6FP23CujWvKNTFNSx3XLeXUOe54+wfOXsjnowd6awCnqoyvj9C+Sf3a10PVUas+MGAS\nrJwGOxdD9ikIjYIhkyFeK81codggzsFREQk2xmSKyB+BbsBfjDEbXJw3pZQXWpqcRpeo0HKPw+bj\nI3Rr2YBuLRvw1LUd2ZJ6hi+3HOHLLUd5+vMtPDN3Cz1bN+S6uGYM79yUpqFW+nOTUpm+eAeHM7Lx\n8RH8fGDWw/3o3DzUFaenvFiHxsGs3nui9A1rsgZtQHwg+6T1+fQhWDDBeq+BXJUrSxD3jDHmMxHp\nDwwFpgP/Anq7NGdKKa+TnpnDxkMZPDGsQ6XSERHiokKJiwpl4vBodqRlsmjzUb7cfIRn52/l2flb\n6d6qAS0aBPHllqOcz7PGMM8vMPj5+LDnWBZxURrEqarVoWkwc5JSOZ2dW+l2mx5rxV+g6JwAudnw\n9XMaxLlAWYYYKezHfz3wtjFmIaCzPSulqtzybdaIQUNjSq9KLSsRoWPTEB4f1oGljw9k2eMDeGJY\nB7Iv5DN34+GLAVyh83kFbh1YWNVehdNv7apt48U5Op1SvuWqUsoSxKWKyFvA7cAiEQko435KKVUu\nS5PTiGoQREcXtkVr1ziYR4e0Z9Fvr0KK2aa4AYeVqowOhT1Ua3MQFxpVvuWqUsoSjN2GNdTHcGNM\nBtAQmOjSXCmlvM65C3ms2n2coZ2aYA855HLFDSxc0oDDSlVU89BA6gf4sfNoLQ7ihkwG/6K/PwID\nyzUhkyqjssydeg5rVoT+9qI8YJcrM6WU8j7f7jrO+bwChlVhVWppJg6PJsjf95JlQf6+TBweXW15\nUN5DROjQpH7tLomLvw1ufAVCWwAC9RoBBg5rX0hXKLVjg4g8C/QAooH3AH/gA6Cfa7OmlPImy5LT\nCA70o1ebhtV2zMIBhAt7pxYdWFipqtahSfDFYXRqrfjbLu3EsOSP8P2r0H44RI9wX75qobL0Tr0Z\nSAA2ABhjDouIDp6klIdwHCKjpgYh+QWG5dvTuTq6Mf6+1dvkdlRCZI27Xqrm6tAkmI/XHuJ41nki\n6pc8bmGtMfgZ2LMC5o+HX6+G+o3cnaNaoyzflheMNa2DARARnUBQKQ8xNymVp+ZsIjUjGwOkZmTz\n1JzNzE1KdXfWyiXp4ClOnL1Qpb1SlfJEhQNI1+p2cUX5BcAtb0POGWvMuGJmilLlV5Yg7lO7d2qY\niDwALANmuDZbSqmymLpwG9m5lw6RkZ2bX+OGyFi6LQ0/H2FQtP6Hrmq3Dk28oIeqM006w9BnYcci\n2PAfd+em1ii1OtUY84KIDMOaMzUamGyMWerynCmlirUrLZPpi3dwLOu80/U1bYiMZclpXNk2nJDA\nWjoAqlK2iPp1aFivDju9LYgD6P1razqur56C1v0h/Ap356jGK0ubOOygbamIRAC1fM4QpTxXyqlz\nvLRsF3M2pFC3jh/BgX5k5uRdtl1NGiJj77Es9hw7y719Wrs7K0q53MUeqt5UnVrIxwdG/Qv+1Qc+\nfwh+8RX4likMUcUotjpVRK4UkW9EZI6IJIjIFmALkCYi2r1EqWp0Ius8zy1IZvALK5n/02HG9WtD\n4qSr+fNNsTV+iIxl26yeekM6NXZzTpSqHtFNgtmVloXxxrZhoZFw/T8hZS2s+qe7c1PjlRQCvwb8\nHxAKLAeuNcb8ICIdgf8BX1VD/pTyalnn83jn273MSNxLdm4+t3ZvwW+Htr9Y0vbzEBnbSc3IoY6v\nD8/fElejelsuS06nU7MQohrUdXdWlKoW7ZsEk3k+jyOnc2pUqXmViRsDO7+Cb6bBFUMgqru7c1Rj\nldSxwc8Ys8QY8xlw1BjzA4AxZnv1ZE0p73U+L5+Zq/Yx4O8reGnZLgZ0aMSS3w3gb2PiL/vSH5UQ\nyXdPDmHSiGgu5BfQrWUDN+W6/E6evcC6AyerdYBfpdwt2hum3yrNdS9AcDOY8wBcOOvu3NRYJQVx\njl3eiraS9sIyYKVcL7/A8Nm6Qwx+YSXPfZFMp2bBzP1NP/51d3faNS55eMabEyIRgdkbas5E08u3\np1NgYFgnDeKU9+jQ2AuHGSkqKAxu/hec3GsNBqwqpKTq1C4icgYQIMh+j/050OU5U8qLGGNYkpzG\nC4t3sCs9i/ioUP42Op7+7SPKnEaz0CD6t4tg9oYUfjukPT4+1TP/aGUsTT5K05BAYiND3J0VpapN\naF1/moYEendJHECbAdB3vDWbQ4cR0GG4u3NU4xRbEmeM8TXGhBhjgo0xfvb7ws9lGgdAREaIyA4R\n2S0iTzpZ31FEVovIeRH5vcPyaBHZ6PA6IyKP2eumiEiqw7rrKnLiSnmK1XtOcPMb3/PQf9eTbwz/\nGtuNeb/pV64ArtDoblGknMpmzf6TLshp1crJzSdx53GGxjSutgnvlfIUHZoGe+cwI0UNfgaaxMK8\n8XD2uLtzU+O4bH4bEfEFXgeuBWKAO0UkpshmJ4EJwAuOC40xO4wxXY0xXYHuwDngc4dNXixcb4xZ\n5KpzUMqVtqSe5t6Za7hzxg+kncnhb6PjWPLYAK6Na1bhoGZ456bUD/Bj9nrPr1JdvecE2bn5DNWq\nVOWFopvUZ1daFvkFXt466eJsDhkwX2dzKC9XTlLYC9htjNlrjLkAfAzc5LiBMSbdGLMWyC0hnSHA\nHmPMAddlVanqs+/4WcZ/tIEbXl3FppQMnr6uEyt+P4jbe7bEr5LzhgbV8eW6uKYs2nyEcxcuHz/O\nkyxJTqNeHV/6XBHu7qwoVe06NAnmfF4Bh06ec3dW3K9JZxjyLOxYCEn/dXduahRXBnGRwCGHzyn2\nsvK6A2tIE0ePisgmEZkpIk674onIgyKyTkTWHTt2rAKHVapqpZ3J4f8+38zQf65k+fZ0Hh3cjsRJ\nV/PAgLYEFhnrrTJGd4vi7IV8Fm89WmVpVrWCAsPX29IYGN2IAL+qO3elagqvnX6rOFc+YrWR+/JJ\nq7ODKhOPHipZROoAI4GnHBb/C/gzVg/ZPwP/AMYV3dcY8zbwNkCPHj20fFZVq7lJqUxfvIPDGdk0\nDQ2kU7Ngvtt9ggJjuOfKVvzm6nY0Cg5wybF7tm5Ii4ZBzF6fys0JUS45RmVtTj1NeuZ5rUpVXqt9\nk/qA1UN1eOembs6NB7g4m0NfmPOgzuZQRq4siUsFWjh8jrKXlce1wAZjTFrhAmNMmjEm3xhTAMzA\nqrZVymPMTUrlqTmbSc3IxgBHTuewfPsx4iJDWP7EIKaM7OyyAA7Ax0e4JSGK7/Yc99g5VJcmp+Hr\nIwzuqLM0KO9Ut44fLRvW1ZI4R6FROptDObkyiFsLtBeRNnaJ2h3A/HKmcSdFqlJFpJnDx5uxpgJT\nymNMX7yD7Nz8y5YfOX2eFg2rZ1aC0d2iMAY+Tyrv/03VY9m2NHq0akBY3TruzopSbtOhifZQvUzc\nGIi71ZrNIXW9u3Pj8VwWxBlj8oDxwGJgG/CpMWariDwsIg8DiEhTEUkBHgf+KCIpIhJir6sHDAPm\nFEn67yKyWUQ2AVcDv3PVOShVEcWVflVnqVjL8Lr0at2Q2RtSPG5+xkMnz7H9aKbO0qC8XnTT+uw9\ndpYLeQWlb+xNLs7m8KDO5lAKV5bEYYxZZIzpYIy5whgz1V72pjHmTfv9UWNMlD3+XJj9/oy97qwx\nJtwYc7pImvcYY+KMMfHGmJHGmCOuPAelyqtZmPOxsKt7jsRbukWy99hZNh7KqNbjlmZpstU6QoM4\n5e06NAkmr8Cw/4QGKpconM3hxB5Y8oy7c+PRXBrEKeWNro9rdtmyIH9fJg6PrtZ8XBffjAA/H4+b\nhmvZtjTaN65Pq/B67s6KUm51sYeqN0+/VZw2A6DPb2Ddu7Bzibtz47E0iFOqiv106DRhQf40DwtE\ngMiwIJ6/JY5RCRUZYafiQgL9Gd65KQt+OsL5vMvb6LnD6XO5/LjvJEO1FE4p2jaqh6+PaLu44gyZ\nDI07w7zf6GwOxdD+u0pVofUHTrFm/0km3xDDuP5t3J0dRnePYv5Ph/l6WzrXOSkhrG7f7Ewnv8Bo\nVapSQICfL20i6mlJXHH8AmD0DHh7kDWbwx0fgk7RdwktiVOqCr2duIfQIH9u79mi9I2rQf92ETQJ\nCfCYabiWJqcRUT+ArlFh7s6KUh4hWnuolkxncyiRBnFKVZE9x7JYkpzGvX1aUS/AMwq5fX2EUQmR\nfLPzGMcyz7s1LxfyCli54xhDOzXGx0f/m1YKrHZxB06eI/uCZzR58Eg6m0OxNIhTqorMSNxLHV8f\n7uvb2t1ZucSYblHkFxjmbXTvmHE/7jtB5vk8naVBKQcZ2ecxBmImf0W/acuZ66FjO7pV4WwOvn4w\n5yHI9+x5oauTBnFKVYH0MznM2ZDKrT2iiKjvutkYKqJ9k2Dio0KZvcG9fxyWJacR6O9Dv3YRbs2H\nUp5iblIqH/1oTTFugNSMbJ6as1kDOWcuzuawBla96O7ceAwN4pSqAu99v5+8ggJ+1b+tu7Pi1Ohu\nUWw7cobkw2fccnxjDEuT07iqfSOC6uiE90qBNbvL+SID/Wbn5jN98Q435cjDxY2B2DGwchqkbnB3\nbjyCBnFKVVJmTi4f/HCAa2Ob0TrCM8c+G9mlOf6+4rYx45KPnOHw6RyGaVWqUhd5wuwuNc71L0D9\nJvZsDufcnRu30yBOqUr635qDZObk8dBAzyyFA2hQrw6DOzZm3sZUcvOrf4qfZcnpiMDVOuG9UhcV\nN4tL/QA/8gs8a7o8jxHUwGofd2IXLNXZHDSIU6oSLuQV8O6qffS9Ipx4Dx82Y3S3KI5nXSBx57Fq\nP/bSbUfp1rIBjYI9q72gUu40cXg0Qf6XNi/w9REyz+dx38w1nMhyb49yj9V2IPQZD2vf8frZHDSI\nU6oS5m5MJe3MeR4aeIW7s1KqQdGNaVivTrVXqR45nc2W1DPaK1WpIkYlRPL8LXFEhgVdnN3lH7d2\n4W+j41iz/yQ3vrqKpIOn3J1Nz1Q4m8OscfDPTjAlDF6MhU2fujtn1cozBrNSqgYqKDC8nbiXTs1C\nGNDe83tc1vHzYWSX5nz040Eyzl0grG6dajnuMp3wXqlijUqIdDolX+fmoTz8wXpue2s1k2+I4e4r\nWyE6W8HP/AKsjg5f/wku2IMlnz4ECyZY7+Nvc1/eqpGWxClVQcu3p7M7PYuHB7atMV+uY7pHcSG/\ngAWbjlTbMZduS6dNRD2uaOSZnT6U8kSxkaF88Wh/+reL4Jl5W3n80590QOCi1s28fFluNiybUu1Z\ncRcN4pSqoDdX7iEyLMgj5iQtq87NQ4huElxt03Bl5uSyes9xhnZqXGMCXaU8RVjdOrx7X08eH9aB\nuRtTufmN79h3/Ky7s+U5ThfzPXYmFaa3g/dvgIVPwJoZsHclZKaBqV0dRrQ6VakKWLf/JOsOnOLZ\nG2Pw9605/wuJCKO7R/LXRdvZcyyLKxrVd+nxEnceJzffMCymqUuPo1Rt5eMjTBjSnq4twvjtx0mM\nfHUVL9zWheGd9XeK0CirCrWowFDoMByO7YRNn8H50w7rwqBRR2gU7fDqCCGRUNo/mps+ha+fs4LH\n0CirXZ6bq201iFOqAt5K3EtYXc+Z6L48RnWNZNqX25m9PoVJIzq69FjLtqXRoK4/3Vp6ds9dpTzd\ngA6NWPBof37z4QYe+u96HhrYlonXRONXg/6JrHJDJltt4HIdxtXzD4LrXvg5uDIGMo/Cse1wbAcc\n32H93LYANvz75/3q1LcCugiHwK5RNIS1sqb92vTppcfykPZ3GsQpVU6707NYmpzGhCHtqVun5v0K\nNQ4JZECHRnyelMoT10Tj66LJ6PPyC1i+PZ0hnRp79x8apapIVIO6fPpwH55bkMxbK/ey6dBpXrkz\nwXuH7ikMnkoqHROBkGbW64qrL93/7PGfg7tjO6z3e5bDTx/9vI1fEES0gxN7Lg0Wwfr89XO1N4gT\nkRHAy4Av8I4xZlqR9R2B94BuwNPGmBcc1u0HMoF8IM8Y08Ne3hD4BGgN7AduM8ZoH2xVbd5O3EOg\nvw/39Wnl7qxU2OhuUTz6vyRW7zlBfxf1rF27/xSns3O5RnulKlVlAvx8mXpzHN1aNuD/Pt/MDa9+\nyxtju9G9VUN3Z8094m+reBBVLwLq9YfW/S9dnp0Bx3c6BHjb4ehm52kU1y6vmrjs32MR8QVeB64F\nYoA7RSSmyGYngQnACzh3tTGma2EAZ3sS+NoY0x742v6sVLVIO5PD50mp3NajBeEeNtF9eQyLaUJw\noJ9Lx4xbti2NOr4+XNW+kcuOoZS3Gt09is8f6Uegvy+3v/UD7323D1PLGu27TVAYtOgF3e6F4VPh\n7tmcC3Lege1ckHvbJrqyjqMXsNsYs9cYcwH4GLjJcQNjTLoxZi2QW450bwIKK7L/DYyqiswqVRYz\nv9tHfoHx2InuyyrQ35cb4pvz1ZajZJ3Pq/L0jTEs25ZG33bh1AuoeVXOStUEMc1DmD++P4OiG/On\nBclM+HgjZ13w++zt9h7L4k/ZYzhnLh1b85ypw99zb3dTriyuDOIiAcduIyn2srIywDIRWS8iDzos\nb2KMKRzk6iigdTWqWpzJyeWjHw5yXVwzWobXdXd2Km1M90iyc/NZtLnqx4zblZ7FgRPndIBfpVws\nNMift+/pzqQR0SzcdJhRr3/H7vQsd2erxtuVlsnLy3Yx4qVEBv9jJZ/k9OHJ3F+RUhBBgRFSCiJ4\nMvdX/Durl1vz6cn/Ivc3xqSKSGNgqYhsN8YkOm5gjDEi4rT82A78HgRo2bKl63Orar2PfjxI5vk8\nHq4BU2yVRbeWDWgTUY/Z61O4rUfV9rJdas/SoFNtKeV6Pj7CI4Pa0SUqjAn/S+Km11Yx/dYuNWoM\nS3czxrDtSCZfbjnCos1H2HPsLCLQo1UDJt8Qw1uJe5h/pj/zL1zafi4yLMhNOba4MohLBRz/MkTZ\ny8rEGJNq/0wXkc+xqmcTgTQRaWaMOSIizYD0YvZ/G3gboEePHtpQQFXK+bx8Zq7aR/92EcRGhro7\nO1VCRLglIZJ/LN3JoZPnaNGw6koXlyan0SUqlCYhgVWWplKqZP3aRfDFhP488uEGHvlwA7/q34Y/\nXNuxRo1lWZ2MMWxKOc2XW47y5ZYjHDhxDh+BK9uGc3/f1gzv3JTG9ndYw3p1eGrOZrJzf541I8jf\nl4nDo92VfcC1QdxaoL2ItMEK3u4A7irLjiJSD/AxxmTa768BnrNXzwfuA6bZP+dVdcaVKmpe0mHS\nM8/zj9u6uDsrVermblYQN2dDKr8d2r5K0kzPzGHjoQyeGNahStJTSpVds9AgPnmwD39dtI13Vu1j\nU8ppXrsr4WIw4u0KCgxJh07x5eajfLnlKKkZ2fj5CH3bRfDwwCu4JqaJ005rhfPbTl+8g8MZ2TQP\nC2Li8Gin895WJ5cFccaYPBEZDyzGGmJkpjFmq4g8bK9/U0SaAuuAEKBARB7D6skaAXxuT9PjB3xk\njPnKTnoa8KmI/BI4AHjHLLfKbQoKDG8m7qFz8xD6t/P8ie7LI6pBXfq0DWdOUgoThrSrkqmxlm+z\nCseHans4pdyijp8PU0Z2JqFlGE/O3sz1r67i9h5RfJ502KMCkOqSX2BYu/8kX9klbmlnzts95yN4\nbGh7hsU0IaxunVLTGZUQ6XHXzKVt4owxi4BFRZa96fD+KFY1a1FnAKdFHsaYE8CQKsymUiVati2N\nvcfO8sqdCbVy/s/R3aP4/Wc/se7AKXq2rvxYU0uT04hqEETHpsFVkDulVEXd1DWSjk1DuPudH3ht\nxZ6Ly1MzsnlqjjXumacFJeU1NynVaelYXn4BP+w9yZdbjrB461GOZ10gwM+HQdGNuDa2GYM7NSYk\n0N/d2a80T+7YoJRHeCtxL1ENgrgutnbOVXhtbFMmz9vC7PUplQ7izl3IY9Xu49zZq2WtDHiVqmmi\nmwY7nTElOzef6Yt31Oggbm5S6iXt1FIzspk0axMfrznIjrRMTp3LJcjfl8GdGnNtbFOujm5c64Y8\nql1no1QVW7v/JOsPnOJPIzvX2qmj6gX4MSK2KQs3HWHKyM4E+vtWOK1Vu45zPq9AhxZRyoMcPZ3j\ndPnhjGyny2uK6Yt3XNLRAOBCfgE/7jvJTV2bMyK2GQM7NCKoTsW/0zxd7fyrpFQVeWvlHhrU9a/y\nITg8zZhuUWSez2Px1qOVSmdpchrBgX70auOlUwAp5YGaFzMMhr+vD9uPnqnm3FSdkoLQl+5IYERs\n01odwIEGcUoVa1daJsu2pXNf39a1/ovgyrbhRIYFMXtDmUcBukx+gWH59nSujm6sQxoo5UEmDo8m\nqEgJu7+v4OcD17+yir8u2lajZno4dfYCU+Zvpbixw4oLWmsj/aZVqhhvJe4l0N+He/u0dndWXM7H\nR7g5IZJVu46RdsZ51UtpNh46xYmzF7QqVSkPMyohkudviSMyLAjBGqB2+pgufPfkEG7tHsXbiXsZ\n+s+VfLXliEfPv5qbX8B73+1j0Avf8J/V++l7RUMC/S8NYzxh7LbqpG3ilHLiyOls5m1MZWzvVjSs\nV3rX89rglm6RvLZiN58npVZoVoolyWn4+QgDo3XCe6U8TXHDY0wbHc+tPaJ4+vMtPPzBBq6ObsSf\nRsZ63NSCK3ak85cvktlz7Cz92oXzzA0xdGwaUmzvVG+hQZxSTrz33X4KDPyyfxt3Z6XatG1Un24t\nw5i9PoWHBrQtd+/SZclpXNk2vFZ021fKm3Rv1ZAvHu3P+9/v58WlOxn24koeHdyOBwa0JcDPvU1J\ndqVl8peF21i58xitw+sy494eDO3U+OL3kyeO3VadtDpVqSJOZ+fy0Y8HuT6uWZVORVUTjO4exa70\nLDanni7XfnuPZbHn2FmtSlWqhvLz9eFXV7Vl2RMDGdKpMS8s2cm1L3/L97uPuyU/Geesdm8jXv6W\nDQdO8fR1nVjyu4EMi2miwxc50CBOqSI+/PEAWefzeGhgW3dnpdrdEN+cOn4+zF6fUq79lm2zl+Kk\n6AAAIABJREFUJrwf0qmxK7KllKomzUKDeGNsd977RU/y8g13vfMjv/04ifTMirWVLa/c/ALe/24f\nA6db7d7u6NmCbyYO4oEBbanjpyFLUVqdqpSDnNx83vtuP1e1j6Bz89ox0X15hAb5MyymCfN/OszT\n18eU+UtzWXI6nZqFENXAu0oulaqtro5uTJ/fhfPGit28uXIvy7enM3F4NGN7t8LXxzUlYcW1e1PF\n07BWKQdzk1I5lnm+Qg37a4sx3aI4dS6X5dvTy7T9ybMXWHfgpFalKlXLBPr78vg10Xz12FXER4Uy\ned5WRr3+HZtSMqr0OLvTM7n/vTX84r215BcYZtzbgw9+2VsDuDLQIE4pW36B4e3EvcRGhtD3inB3\nZ8dtrmofQaPgAGZvKFuV6vLt6RQYGNZJgzilaqO2jerzwS9788qdCRw9k8NNr3/HM3O3cDo7t1Lp\nFrZ7G/7St6zfr+3eKkKrU5WyLU1OY+/xs7x2V+2c6L6s/Hx9GNW1Oe99t58TWecJrx9Q4vbLktNo\nGhJIbKT+16xUbSUijOzSnEHRjfjnkp38Z/V+vtxyhKev78SorpHl+s7MzS/gwx8O8OKyXWTm5HJn\nr5Y8PqxDqd816nJaEqcUYIzhzZV7aNmwLiM6186J7stjdPco8goM8386XOJ2Obn5JO46xtCYxl4d\n+CrlLUIC/ZkysjPzx/cnskFdfvfJT9w140d2p2eVaf9vdqRz7cvfMmVBMp2bh7BwwlVMvTlOA7gK\n0iBOKWDt/lNsPJTBA1e1qbUT3ZdHx6YhdG4eUmqV6uo9Jzh3IZ+hWpWqlFeJjQxlzq/78pdRsWw9\nfJprX05k+uLtZF/Id7r97vQs7n9vDfe/t5bc/ALevqc7H/6qN52aaQl+ZWh1qlLAmyv3EF6vDrfW\n8onuy2N0tyie+yKZHUcziW4a7HSbpdvSqFfHlz5e3IZQKW/l6yPcfWUrhnduyvOLtvH6ij3M23iY\nEbFN+XLzUQ5nZNM0NJB2jevz/Z4T1PX35enrOnFv31ZuH0S4ttAgTnm9HUczWb49nceHdSDQX79Y\nCt3UtTl/XbSN2RtS+L/rOl22vqDAsCw5jYHRjfQLWVWr3NxcUlJSyMmpnrHLVOkeiA/g3phWnDyb\nS35BDr0HhAFhP6+Pa07DenXw9TnP3l073ZdRDxMYGEhUVBT+/hWb6UaDOOX13k7cS5C/L/dc2crd\nWfEo4fUDGBTdmM+TUpk0PPqyaubNqadJzzyvVamq2qWkpBAcHEzr1q21LaaH2XbkDLn5BZctr+Pr\nQ0etOr2EMYYTJ06QkpJCmzYVm+JRG/8or3Y4w5ro/o5eLWjgJRPdl8eY7pEcyzzPt06m3lmanIav\njzC4o87SoKpXTk4O4eHhGsB5IGcBHMCFYpZ7MxEhPDy8UiXKLg3iRGSEiOwQkd0i8qST9R1FZLWI\nnBeR3zssbyEiK0QkWUS2ishvHdZNEZFUEdlov65z5Tmo2m3mqn0YvGui+/K4umNjwur6O52Ga9m2\nNHq0akBYXQ1+VfXTAM4z1SmmY1hxy71dZZ9jl11VEfEFXgeuBWKAO0UkpshmJ4EJwAtFlucBTxhj\nYoArgd8U2fdFY0xX+7XINWegarvT53L535qD3BjfTKeLKkaAny8juzRnSXLaJQN7Hjp5ju1HM3WW\nBqXUJZqEBuJTJDDxEaFJaKCbclS7uTI07gXsNsbsNcZcAD4GbnLcwBiTboxZC+QWWX7EGLPBfp8J\nbAMiXZhX5YU++PEAZy/k8+AA751iqyxGd4viQl4BCzcdubhsabI14b0Gccpb7d+/n9jYWJek/c03\n33DDDTcAMH/+fKZNm+aS47hCg7p1iGwQdLHkrY6vD5ENgmhQwRL78l7n999/n8OHSx7f8v3332f8\n+PEVyo+ncWUQFwkccvicQgUCMRFpDSQAPzosflRENonITBFpUMx+D4rIOhFZd+zYsfIeVtVy1kT3\n+xjYoRExzbWxbUnio0Jp17j+JWPGLduWRvvG9WkVXs+NOVOqbOYmpdJv2nLaPLmQftOWMzcp1d1Z\nKrORI0fy5JOXtUbyTJs+hRdjafD3xnT8uC/xJ5fQsVlIhQO4iihLEOcqeXl51X5Mj66kFpH6wGzg\nMWPMGXvxv4C2QFfgCPAPZ/saY942xvQwxvRo1KhRteRX1RxzNqRyPOsCDw1s6+6seDwRYXS3KNYf\nOMW+42c5fS6XH/edZKiWwqkaYG5SKk/N2UxqRjYGSM3I5qk5m6skkMvLy2Ps2LF06tSJMWPGcO7c\nOZ577jl69uxJbGwsDz74IMYYAF555RViYmKIj4/njjvuAODs2bOMGzeOXr16kZCQwLx58y47hmOp\n0f3338+ECRPo27cvbdu2ZdasWRe3mz59Oj179iQ+Pp5nn3220udWbps+hQUT4PQhwFg/F0ywlldS\nWa/zrFmzWLduHWPHjqVr165kZ2ezdu1a+vbtS5cuXejVqxeZmZkAHD58mBEjRtC+fXsmTZp08Vj1\n69fn6aefpkuXLlx55ZWkpVm1Dvv372fw4MHEx8czZMgQDh48CFj35OGHH6Z3795MmjSJKVOmcN99\n93HVVVfRqlUr5syZw6RJk4iLi2PEiBHk5lZuvtmiXDnESCrgOHJqlL2sTETEHyuA+9AYM6dwuTEm\nzWGbGcAXlc+q8ib5BYYZ3+4lPiqUPm11kNqyuDkhkumLtzNnQwrtGtcnv8BoVaryCH9asJXkw2eK\nXZ90MOOynpHZuflMmrWJ/6056HSfmOYhPHtj51KPvWPHDt5991369evHuHHjeOONNxg/fjyTJ08G\n4J577uGLL77gxhtvZNq0aezbt4+AgAAyMjIAmDp1KoMHD2bmzJlkZGTQq1cvhg4dWuIxjxw5wqpV\nq9i+fTsjR45kzJgxLFmyhF27drFmzRqMMYwcOZLExEQGDBhQ6jmU2ZdPwtHNxa9PWQv55y9dlpsN\n88bD+n8736dpHFxbelVxWa/zmDFjeO2113jhhRfo0aMHFy5c4Pbbb+eTTz6hZ8+enDlzhqCgIAA2\nbtxIUlISAQEBREdH8+ijj9KiRQvOnj3LlVdeydSpU5k0aRIzZszgj3/8I48++ij33Xcf9913HzNn\nzmTChAnMnTvXOvWUFL7//nt8fX2ZMmUKe/bsYcWKFSQnJ9OnTx9mz57N3//+d26++WYWLlzIqFGj\nSr/eZeTKkri1QHsRaSMidYA7gPll2VGs7hrvAtuMMf8ssq6Zw8ebgS1VlF/lJZZsPcq+42d5eOAV\n2sOtjJqGBtKvXQRzNqSyZGsaEfUD6BoVVvqOSrlZcUNbVMWQFy1atKBfv34A3H333axatYoVK1bQ\nu3dv4uLiWL58OVu3bgUgPj6esWPH8sEHH+DnZ5WfLFmyhGnTptG1a1cGDRpETk7OxRKe4owaNQof\nHx9iYmIulhItWbKEJUuWkJCQQLdu3di+fTu7du2q9PmVS9EArrTl5VCe6+xox44dNGvWjJ49ewIQ\nEhJy8doPGTKE0NBQAgMDiYmJ4cCBAwDUqVPnYnvE7t27s3//fgBWr17NXXfdBVhB46pVqy4e59Zb\nb8XX9+cBz6+99lr8/f2Ji4sjPz+fESNGABAXF3cxvarispI4Y0yeiIwHFgO+wExjzFYRedhe/6aI\nNAXWASFAgYg8htWTNR64B9gsIhvtJP/P7on6dxHpChhgP/CQq86hLOYmpTJ98Q4OZ2TTPCyIicOj\nGZXgmj4YtfFY1X9O20nNyMHXR8gpZo4/5Vzr8Lp8u+s4qRnZ1K3jy/yfDrvsXilVVqWVmPWbtpzU\njOzLlkeGBfHJQ30qdeyi/wSKCI888gjr1q2jRYsWTJky5eIYYAsXLiQxMZEFCxYwdepUNm/ejDGG\n2bNnEx0dfUk6hcGZMwEBP08UX1hVa4zhqaee4qGHXPjnsLQSsxdj7arUIkJbwC8WVurQ5bnOZeV4\nHX19fS+2Z/P39794PMflJalX79K2wYVp+/j4XJKej49Plbebc+mMDXbQtajIsjcd3h/FqmYtahXg\ntIjEGHNPVeaxMgrbWmTnWsFAakY2f5i9iX3HsxjQoWrb4SXuPMabK/dyPq+g1hzLneeUX2B4eu4W\nfHxEA5EymJuUymcOY8Wdu5DPU3OsqhW9fsqTTRwefcn3NECQvy8Th0eXsFfZHDx4kNWrV9OnTx8+\n+ugj+vfvz/fff09ERARZWVnMmjWLMWPGUFBQwKFDh7j66qvp378/H3/8MVlZWQwfPpxXX32VV199\nFREhKSmJhISEcudj+PDhPPPMM4wdO5b69euTmpqKv78/jRtX40DcQyZbbeByHQJm/yBreSWV9ToD\nBAcHX2z3Fh0dzZEjR1i7di09e/YkMzPzYnVqefXt25ePP/6Ye+65hw8//JCrrrqq0udVFXTarUqY\nvnjHJV8MAOfzCnj56928/PVulx+/Nh6rOs8pOzef6Yt3aBBSBtMX7yAn9/J2RXr9lKcrfD5dUeIf\nHR3N66+/zrhx44iJieHXv/41p06dIjY2lqZNm16sxsvPz+fuu+/m9OnTGGOYMGECYWFhPPPMMzz2\n2GPEx8dTUFBAmzZt+OKL8jfzvuaaa9i2bRt9+lgli/Xr1+eDDz6o3iAu/jbr59fPwekUCI2yArjC\n5ZVQ1usMP3c0CAoKYvXq1XzyySc8+uijZGdnExQUxLJlyyqUh1dffZVf/OIXTJ8+nUaNGvHee+9V\n+ryqghQWx9ZmPXr0MOvWravydNs8uZDirt5/xvWq0mPdO3NNsetq6rE84ZwE2Dft+io9Vm1U3LOu\n10+5w7Zt2+jUqZO7s6FUlXD2PIvIemNMj9L21ZK4SmgeFlRsW4uqrg6MrIXH8oRzah5WsaJ1b1Pc\ns67XTyml3Mejx4nzdBOHRxPk73vJsqpqa+ENx6qN51Rb6fVTSinPoyVxleDKthbecKzaeE61lV4/\n5WmMMTpEkKrxKtukTdvEKaWUqlH27dtHcHAw4eHhGsipGssYw4kTJ8jMzKRNmzaXrNM2cUoppWql\nqKgoUlJS0HmxVU0XGBhIVJSzkdbKRoM4pZRSNYq/v/9lJRdKeSPt2KCUUkopVQNpEKeUUkopVQNp\nEKeUUkopVQN5Re9UETkGHHCyKhQ4Xcxu5V0XARyvUAZdq6TzcHfa5d2/rNuXtl1F1xe3XO+96/fV\ne18+rrr31f07X559KnPvK7JO771r96+q+17aNp5671sZY0of9d4Y47Uv4O2qWgesc/f5lPc83J12\nefcv6/albVfR9SUs13vv4n313nvGva/u3/nquvcVWaf33rX7V9V9r+333turUxe4YJ2ncWVeK5t2\nefcv6/albVfR9TXpvoPn3vuK7Kv3vnxcld/q/p0vzz6Vufe15fseas+9r6r7Xto2Nfree0V1anUQ\nkXWmDAPzqdpH77330nvvvfTeey9PuvfeXhJXld52dwaU2+i99156772X3nvv5TH3XkvilFJKKaVq\nIC2JU0oppZSqgTSIU0oppZSqgTSIU0oppZSqgTSIU0oppZSqgTSIqyYiUk9E1onIDe7Oi6o+ItJJ\nRN4UkVki8mt350dVHxEZJSIzROQTEbnG3flR1UdE2orIuyIyy915Ua5l/23/t/27Pra6j69BXClE\nZKaIpIvIliLLR4jIDhHZLSJPliGpPwCfuiaXyhWq4t4bY7YZYx4GbgP6uTK/qupU0b2fa4x5AHgY\nuN2V+VVVp4ru/V5jzC9dm1PlKuV8Bm4BZtm/6yOrPa86xEjJRGQAkAX8xxgTay/zBXYCw4AUYC1w\nJ+ALPF8kiXFAFyAcCASOG2O+qJ7cq8qointvjEkXkZHAr4H/GmM+qq78q4qrqntv7/cP4ENjzIZq\nyr6qhCq+97OMMWOqK++qapTzGbgJ+NIYs1FEPjLG3FWdefWrzoPVRMaYRBFpXWRxL2C3MWYvgIh8\nDNxkjHkeuKy6VEQGAfWAGCBbRBYZYwpcmW9VeVVx7+105gPzRWQhoEFcDVBFv/cCTMP6gtcAroao\nqt97VXOV5xnACuiigI24oXZTg7iKiQQOOXxOAXoXt7Ex5mkAEbkfqyROA7iaq1z33g7gbwECgEUu\nzZlytXLde+BRYCgQKiLtjDFvujJzyqXK+3sfDkwFEkTkKTvYUzVbcc/AK8BrInI9bphrVYO4amSM\ned/deVDVyxjzDfCNm7Oh3MAY8wrWF7zyMsaYE1htIVUtZ4w5C/zCXcfXjg0Vkwq0cPgcZS9TtZ/e\ne++l99576b1XHvkMaBBXMWuB9iLSRkTqAHcA892cJ1U99N57L7333kvvvfLIZ0CDuFKIyP+A1UC0\niKSIyC+NMXnAeGAxsA341Biz1Z35VFVP77330nvvvfTeq5r0DOgQI0oppZRSNZCWxCmllFJK1UAa\nxCmllFJK1UAaxCmllFJK1UAaxCmllFJK1UAaxCmllFJK1UAaxCmllFJK1UAaxCmlKkxEXhSRxxw+\nLxaRdxw+/0NEHi8lje/LcJz9IhLhZPkgEelbzD4jReTJUtJtLiKz7PddReS6cu5/v4i8Zr9/WETu\nLe1cSjuHiqbjCnbevnB3PpRSzuncqUqpyvgOuA14SUR8gAggxGF9X+B3JSVgjHEahJXRICALuCwQ\nNMbMp5QR1Y0xh4Ex9seuQA9gUVn3L5JWRSe4H4TDOVQiHaWUl9GSOKVUZXwP9LHfdwa2AJki0kBE\nAoBOwAYAEZkoImtFZJOI/KkwARHJsn/6iMgbIrJdRJaKyCIRGeNwrEdFZIOIbBaRjiLSGmuS8d+J\nyEYRucoxY0VKyd4XkVdE5HsR2VuYroi0FpEt9jQ6zwG322ndXmT/G0XkRxFJEpFlItKk6IUQkSki\n8nu7dG+jwytfRFo5S8PZORSmY6fZVUR+sK/Z5yLSwF7+jYj8TUTWiMjOoudub9NMRBLtdLcUbiMi\nI+zr+JOIfG0v6yUiq+28fS8i0U7SqyciM+1jJonITcU+FUqpaqFBnFKqwuySrDwRaYlV6rYa+BEr\nsOsBbDbGXBCRa4D2QC+sEq/uIjKgSHK3AK2BGOAefg4OCx03xnQD/gX83hizH3gTeNEY09UY820p\n2W0G9AduAKYVOY8LwGTgEzutT4rsuwq40hiTAHwMTCruIMaYw3YaXYEZwGxjzAFnaZThHP4D/MEY\nEw9sBp51WOdnjOkFPFZkeaG7gMV2ProAG0WkkZ2n0caYLsCt9rbbgavsvE0G/uokvaeB5fYxrwam\ni0i94q6DUsr1tDpVKVVZ32MFcH2BfwKR9vvTWNWtANfYryT7c32soC7RIZ3+wGfGmALgqIisKHKc\nOfbP9VgBX3nNtdNOdlaSVooo4BMRaQbUAfaVtoOI9AMewDqvcqchIqFAmDFmpb3o38BnDps4Xo/W\nTpJYC8wUEX+sc98oIoOARGPMPgBjzEl721Dg3yLSHjCAv5P0rgFGFpYSAoFAS6x5JJVSbqAlcUqp\nyvoOK2iLw6pO/QGrFK0vP7dVE+D5whIqY0w7Y8y75TzOeftnPhX7B/S8w3sp576vAq8ZY+KAh7AC\nmGLZgdq7wG3GmKyKpFEGJV4PY0wiMABIBd4vpbPEn4EVxphY4MZi8iZYJXiF97ClMUYDOKXcSIM4\npVRlfY9VRXnSGJNvl+6EYQVyhUHcYmCciNQHEJFIEWlcJJ3vgNF227gmWA3+S5MJBFfBOZSWVihW\nMARwX0mJ2CVfn2FVg+4sQxpOj2uMOQ2ccmjvdg+wsuh2JeSjFZBmjJkBvAN0wwqwB4hIG3ubhk7y\ndn8xSS7Gapco9r4JZc2LUso1NIhTqoYSkSwRaevufGC11YrAChAcl502xhwHMMYsAT4CVovIZmAW\nlwcus4EUIBn4AKtDxOlSjr0AuNlZx4YKWAHEFHZsKLJuCvCZiKwHjpeSTl+s9oB/cujc8Bt7nbM0\nip5DV6Bw+/uw2p5tspc/V9pJiEhLu7PI1cBPIpIE3A68bIw5BjwIzBGRn4B59rYvAM/b2xYt1Rth\nP2d/xqpm3SQiW+3PjsdtLSJGRPzsz1+KSIkBb0WIyFa7Wtij2R1jVrk7H6p2E2OMu/OglFuIyH6g\nCVZ1VC5WqdHDxphDVZDur4wxy4pZPwj4wBgTVZnj1EYiUt8YkyUi4cAaoJ8x5qi781WdROR+rOen\nfzHrv8F6ft5xtr6Sx65w2nZP232AvzEmr4ry8z6QYoz5Y1WkV51Ku49KVQUtiVPe7kZjTH2snotp\nWO2W3K6wNKM2KuXcvhCRjcC3wJ+9LYBTSqny0CBOKcAYk4NVxRdTuExEAkTkBRE5KCJpIvKmiATZ\n6yJE5AsRyRCRkyLyrd2W679YPfYW2NWdlwxFYQ/J8CXQ3F6fJda4YlNEZJaIfCAiZ4D7HcbuyhCR\nIyLymljjmRWmZUSknf3+fRF5XUQWikimWOORXVHc+YrIZyJyVEROizWWWGeHdUFizbRwwF6/yuG8\n+4s1jliGiByySxsKxy37lUMal1Ql2Xn9jYjsAnbZy1620zgjIutF5CpjzCB7SIw4+xrtsc9nvYi0\nsM/xH0XOZb6IXDagsIj8S0ReKLJsntgzSIjIH0Qk1U5/h4gMcZJGG/tcfezPM0Qk3WH9f8WesUJE\nQkXkXftepYrIX0TEt5jrcY19zNNijY230vH62du8ICKnRGSfiFxrL5sKXAW8Zj87rznJc9FqzW9E\n5M8i8p19rkvEnv3Ccdvi0i7ynF0v1hhxZ+x7N6Xo8R3ycfGZEGtMuiyHlxG7SrS4Z1FEHgTGApPs\nfRbYy/eLyFD7fYCIvCQih+3XS2KNT1g420SKiDwhIun2fflFCfm9X6wxBDPtaz7WYd0DIrLNXpcs\nIt3s5U86PKPJInJzCel3FGv8w5P2vb+tuG2VKjNjjL705ZUvYD8w1H5fF2sIh/84rH8Ra8T+hljt\ntxZg9bAEeB5rfC9/+3UVPzdPuJhuMccdhFVF5LhsClaV7iisf66CgO7AlVhtlFpjDeXwmMM+Bmhn\nv38fOIE1Dpsf8CHwcQl5GGefUwDwErDRYd3rwDdYQ4X4YrXxCgBaYTXCv9M+53Cgq73PN1hVR4Vp\n3A+sKpLXpfa1DLKX3W2n4Qc8ARwFAu11E7Ha1UVj9YrsYm/bCzgM+NjbRQDngCZOznEAcMjhvjQA\nsoHmdrqHgOb2utbAFcVcq4NAd/v9DmAv0MlhXYL9/nPgLaAe0BirOvihotfDzvMZrGFS/IDf2vf+\nVw7b5mINT+IL/No+Z3F2rZ3kt7V9vf0ctt8DdMB6rr4BppWw7a+KpOf4nA3CCrB9gHis0utRZU3L\nXv4g1rh0IWV4Ft8H/lLC7+1zWG0xGwONsJpE/Nkhr3n2Nv7AdVjPSgMneapn35No+3MzoLP9/las\nTh89sZ7FdkArh3XN7etxO3AWaObkntfDet5+Yd/zBKx2kTHu/h7UV81+uT0D+tKXu172H4MsIMP+\no3kYiLPXif2FfIXD9n2Affb754B5hX/cnKRbkSAusZT8PgZ87vC5aBD3jsO664DtZbwOYXZaofYf\no2ygi5PtnnI8fpF1l/zBxnkQN7iUfJwqPC5WsHRTMdttA4bZ78cDi4rZTrCCrAH25wewBqvF/kOc\nDgzFasNVUr7+CzwONLXz9XesWRba2M+OD1bbyvPYAaq9351Yw3YU/YN+L7C6SD4PcWkQt9thfV37\n+jV1dq2d5Lc1lwdTf3RY/wjwVQnbFhvEOTnWS1gDFZc1rf72de9Q2rPo8FyXFMTtAa5zWDcc2O/w\ne5ZdmB97WTrWgMtFj1vPvpejHe+hvW4x8Nsy/i5txH5ui9zz24Fvi2z7FvBsWdLVl76Ke2l1qvJ2\no4wxYVjjYo0HVopIU6z/6usC6+3qtAzgK3s5wHRgN7DEroIpcaL0MrqkQ4WIdBCryvaoWFWsf8Uq\nxSmOY/uxc1gD6l5GRHxFZJpdDXQG648idtoRWNdij5NdWxSzvKyKnt/v7Sqq0/b1DeXn8yvpWP/G\nKsXD/vlfZxsZYwzWzAh32ovuwiqhxBizGysongKki8jHItK8mOOtxAoIBmANTvwNMNB+fWusAYRb\nYZX2HHF4Xt7CKiEqqjkO18LOZ0qRbY46rD9nv3V6P8uoTM9GaUSkt4isEJFjInIaK5gt6Zl03LcF\n8Clwn7GHXinlWSyL5sABh88H7GWFTphLO1k4PXdjzFmsQOthrHu4UEQ62quLfRZF5F6xehUX3vPY\nYvLeCuhduJ297VisfwyUqjAN4pQCjDW+2Rysnqr9sao6srGqVMLsV6ixOkFgjMk0xjxhjGkLjAQe\nl5/bVJXW5bu49UWX/wur2qm9MSYE+D/KP0itM3cBN2GVQoXy82j/gnXeOYCz9nSHilkOVqllXYfP\nzv44XTw/sYbSmATchlW9FYY1nEjh+ZV0rA+Am0SkC9bcrHOL2Q7gf8AYscZM6401jImVGWM+MlbP\nwVZ23v5WTBorsarLB9nvVwH9sIK4wnHbDmGVxEU4PC8hxpjOTtI7gjV7AwAiIo6fy6C056sySkv7\nI6wmBi2MMaFYTQpKfSbFalM5F3jJGPOlw6qSnsWy5Ocw1v0r1NJeVm7GmMXGmGFYVanbsaYng2Ke\nRfuZmoH1z1+4/Qxvwfn1OASsdHg2wowx9Y0xv65IXpUqpEGcUlh/SMWa0LsBsM0uXZkBvCj2oLRi\nDVA73H5/g4i0s/8An8YK/grs5NKAksZvSwPCxZpWqSTBWO10suxSgar6wg/GCjhOYAVeF+fJtM97\nJvBPsTpc+IpIH7ux+IfAUBG5zW4IHy4iXe1dNwK3iEhduxH8L8uQhzzgGOAnIpOBEIf17wB/FpH2\n9r2JF2vYEYwxKVhTSv0Xa17S7OIOYoxJwgpM38GaRzQDQESiRWSwfV45WAF7QTFp7LLX3431h/gM\n1j0cjR3EGWOOAEuAf4hIiFidXK4QkYFOklwIxInIKLE6H/yG8pXIlPZ8VUZpaQdjDeoNP5j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XrzqarvVx9CAQNbVclZHBYrtHgWatwNMwfD9Mdh12zo+ikULmnzJfc2qoCHVfHSH//R/4f1/Dig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\n7iDemLGN+dtO0DWknJMCvTFlz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n0mI0i2xJ+yfTwl0UybCpENR85cYO7W48zZEsXuE3FY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\nmsQppZRSSoWg/weFlZvucYqklAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x200719d5f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "  best_train_accs.append(max(solvers[ws].train_acc_history))\n",
    "  bn_best_train_accs.append(max(bn_solvers[ws].train_acc_history))\n",
    "  \n",
    "  best_val_accs.append(max(solvers[ws].val_acc_history))\n",
    "  bn_best_val_accs.append(max(bn_solvers[ws].val_acc_history))\n",
    "  \n",
    "  final_train_loss.append(np.mean(solvers[ws].loss_history[-100:]))\n",
    "  bn_final_train_loss.append(np.mean(bn_solvers[ws].loss_history[-100:]))\n",
    "  \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(10, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question:\n",
    "Describe the results of this experiment, and try to give a reason why the experiment gave the results that it did."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Answer:\n",
    "From the first 2 plots it is clear that batch norm trained network is more robust to the scale of weight initialisation. \n",
    "While the baseline model only starts learning around weight initialisation of around 10^-2 the batchnorm model starts to learns all the way from 10^-4. \n",
    "Also from the last plot the baseline model training loss explodes when the weight initialisaiton is too large while this does not happen to the batchnorm network.\n",
    "\n",
    "The reason for these behaviours is that the batchnorm layer is normalising outputs of each layer so that activations are prevented from becoming too large or too small. This does not happen with the normal model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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